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evaluate.py
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evaluate.py
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import matplotlib.pyplot as plt
import pandas as pd
from cdrift import evaluation
from cdrift.utils.helpers import readCSV_Lists, convertToTimedelta, importLog
import numpy as np
from datetime import datetime
from statistics import mean, harmonic_mean, stdev
from scipy.stats import iqr
from typing import List, Tuple
import seaborn as sns
import re
import os
from pathlib import Path
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
from tqdm.auto import tqdm
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
mapping_ostovar_to_shortnames = {
"ConditionalMove": 'cm',
"ConditionalRemoval": 'cre',
"ConditionalToSequence": 'cf',
"Frequency": 'fr',
"Loop": 'lp',
"ParallelMove": 'pm',
"ParallelRemoval": 'pre',
"ParallelToSequence": 'pl',
"SerialMove": 'sm',
"SerialRemoval": 'sre',
"Skip": 'cb',
"Substitute": 'rp',
"Swap": 'sw',
}
shorter_names = {
"Zheng DBSCAN": "RINV",
"ProDrift": "ProDrift",
"Maaradji Runs": "ProDrift",
"Bose J": "J-Measure",
"Bose WC": "Window Count",
"Earth Mover's Distance": "EMD",
"Process Graph Metrics": "PGM",
"Martjushev ADWIN J": "ADWIN J",
"Martjushev ADWIN WC": "ADWIN WC",
"LCDD": "LCDD"
}
used_parameters = {
"Bose J": ["Window Size", "SW Step Size"],
"Bose WC": ["Window Size", "SW Step Size"],
"Martjushev ADWIN J": ["Min Adaptive Window", "Max Adaptive Window", "P-Value", "ADWIN Step Size"],
"Martjushev ADWIN WC": ["Min Adaptive Window", "Max Adaptive Window", "P-Value", "ADWIN Step Size"],
"ProDrift": ["Window Size", "SW Step Size"],
"Earth Mover's Distance": ["Window Size", "SW Step Size"],
"Process Graph Metrics": ["Min Adaptive Window", "Max Adaptive Window", "P-Value"],
"Zheng DBSCAN": ["MRID", "Epsilon"],
"LCDD": ["Complete-Window Size", "Detection-Window Size", "Stable Period"]
}
used_parameters = {
shorter_names[name]: used_parameters[name]
for name in used_parameters.keys()
}
def split_by_name(df):
return [
(alg, df[df["Algorithm"] == alg].copy())
for alg in df["Algorithm"].unique()
]
def map_row_to_cp(row):
logname = row["Log"]
if row["Log Source"] == "Ceravolo":
return logname.split("_")[-1]
elif row["Log Source"] == "Ostovar":
change_pattern = logname.split("_")[-1]
if change_pattern.isnumeric(): # This log has a noise level; Use the second to last string instead
change_pattern = logname.split("_")[-2]
return mapping_ostovar_to_shortnames[change_pattern]
elif row["Log Source"] == "Bose":
return None
else:
raise ValueError(f"Unknown Log Source for Versatility: {row['Log Source']}; Is this Log meant to be used for a Versatility Evaluation?")
def map_row_to_noise_level(row):
logname = row["Log"]
if row["Log Source"] == "Ceravolo":
return re.search('noise([0-9]*)_', logname).group(1)
elif row["Log Source"] == "Ostovar":
# return mapping_ostovar_to_shortnames[logname.split("_")[-1]]
last_member = logname.split('_')[-1]
if last_member.isnumeric():
return last_member
else:
return '0'
elif row["Log Source"] == "Bose":
return None
else:
raise ValueError(f"Unknown Log Source for Noise Level: {row['Log Source']}; Is this Log meant to be used for a Robustness Evaluation?")
def preprocess(_df):
df = _df.copy()
## Exclude Martjushev WC (in favor of LCDD)
df = df[df["Algorithm"] != "Martjushev ADWIN WC"]
## Rename Approaches to shorter names
df["Algorithm"] = df["Algorithm"].map(lambda x: shorter_names.get(x, x))
## Only use Atomic Logs
atomic_logs = {
log
for _, (source, log) in df[["Log Source", "Log"]].iterrows()
if (source.lower() == "ostovar" and log.lower().startswith("atomic")) # Only use atomic logs from ostovar
or (log.lower().startswith("bose")) # Use bose log
or (source.lower() == "ceravolo" and set(log.lower().split("_")[-1]) != {'i', 'o', 'r'}) # Exclude all composite (IOR, ROI, etc.) logs from ceravolo
}
df = df[df["Log"].isin(atomic_logs)]
# Add a column stating the change pattern that is applied and one for the noise level
# Only use the Ceravolo and Ostovar Logs as Bose has only 1 log, i.e., no noise levels
df["Change Pattern"] = df.apply(lambda x: map_row_to_cp(x), axis=1)
df["Noise Level"] = df.apply(lambda x: map_row_to_noise_level(x), axis=1)
return df
def evaluate(csv_path="algorithm_results.csv", out_path="Evaluation_Results", lag_window:int=200, min_support:int=1, verbose:bool = False):
"""Evaluate the results of the algorithms.
Args:
csv_path (str, optional): Path to results csv file. Defaults to "algorithm_results.csv".
out_path (str, optional): Directory in which to save results. Defaults to "Evaluation_Results".
lag_window (int, optional): Max acceptable distance to a ground truth change point to be classified as true positive. Defaults to 200.
min_support (int, optional): Minimum support for latency calculation --> a parameter setting must have at least 3 instances where a true positive was detected before it can be deemed the "best parameter setting" for latency calculation. Defaults to 1.
verbose (bool, optional): Whether to print information about the data. Defaults to False.
Raises:
ValueError: CSV Contains duplicate rows
"""
if not os.path.exists(out_path):
os.makedirs(out_path)
# Preprocess
df = readCSV_Lists(csv_path)
df = preprocess(df)
ALPHA_SORT_NAMES = sorted(list(df["Algorithm"].unique()))
## Calculate Log Lengths for Scalability
_groupkeys = list(df.groupby(["Log Source", "Log"]).groups.keys())
_logpaths = [Path("EvaluationLogs", source, f"{log}.xes.gz") for source, log in _groupkeys]
loglengths = dict()
loglengths_events = dict()
for logpath in tqdm(_logpaths, "Calculating Log Lengths. Completed Logs: "):
log = importLog(logpath.as_posix(), verbose=False)
loglengths[logpath] = len(log)
loglengths_events[logpath] = len([evt for case in log for evt in case])
if verbose:
print(f"Number of Logs: {len(loglengths.keys())}")
print(f"Number of Cases: {sum(loglengths.values())}")
print(f"Number of Events: {sum(loglengths_events.values())}")
num_drifts = sum(
len(group.iloc[0]["Actual Changepoints for Log"])
for name, group in df.groupby(["Log Source", "Log"])
)
print("Total number of drifts:", num_drifts)
dfs = split_by_name(df)
if pd.read_csv(csv_path).duplicated().any():
raise ValueError("CSV contains duplicate rows!")
## Split into Noisy/Noiseful Logs
logs = zip(df["Log Source"], df["Log"])
noiseless_logs = {
log for source, log in logs if
(source == "Ostovar" and not (log.endswith("_2") or log.endswith("_5"))) or
(source == "Ceravolo" and log.split("_")[2]=="noise0") or
source == "Bose"
}
df_noiseless = df[df["Log"].isin(noiseless_logs)]
df_noisy = df[df["Log"].isin(noiseless_logs) == False]
dfs_noisy = split_by_name(df_noisy)
dfs_noiseless = split_by_name(df_noiseless)
## Define Color Scheme
colors = ["#DB444B","#9A607F","#006BA2","#3EBCD2","#379A8B","#EBB434","#B4BA39","#D1B07C"]
color_map = {ALPHA_SORT_NAMES[i]:colors[i] for i in range(0,len(ALPHA_SORT_NAMES))}
analyze_change_pattern_distribution(df, out_path)
# Accuracy
accuracies, computed_accuracy_dicts, computed_precision_dicts, computed_recall_dicts, accuracy_best_param = calculate_accuracy_metric_df(df_noiseless, lag_window, show_progress_bar=True)
plot_accuracy(computed_accuracy_dicts, computed_precision_dicts, computed_recall_dicts, accuracy_best_param, out_path, lag_window, colors, ALPHA_SORT_NAMES)
# Latency
latencies, scaled_latency_dicts, computed_latency_dicts, best_params_latency = calculate_latency(df_noiseless, lag_window, min_support=min_support, show_progress_bar=True)
plot_latency(computed_latency_dicts, best_params_latency, colors, out_path, lag_window, ALPHA_SORT_NAMES)
# Versatility
## Only use Ceravolo and Ostovar Logs because these are the only ones that have different change patterns.
## (Theoretically we could also drop all rows containing a nan in the change pattern column? This would be more general)
df_v = df_noiseless[df_noiseless["Log Source"].isin(["Ceravolo", "Ostovar"])].copy(deep=True)
versatilities, versatility_recall_dicts, best_params_versatility = calc_versatility(df_v, lag_window, show_progress_bar=True)
plot_versatility(versatility_recall_dicts, best_params_versatility, out_path, lag_window, colors, ALPHA_SORT_NAMES)
# Scalability
scalabilities = calculate_scalability(df, show_progress_bar=True)
plot_scalability(scalabilities, dfs, out_path, lag_window, colors, ALPHA_SORT_NAMES)
df_seconds, seconds_per_case, seconds_per_event = calculate_rel_scalabilities(df, loglengths, loglengths_events)
plot_rel_scalability(df_seconds, out_path, lag_window, colors, ALPHA_SORT_NAMES)
# Parameter Sensitivity
sensitivities = calculate_parameter_sensitivity(df, versatility_recall_dicts, computed_accuracy_dicts, scaled_latency_dicts, show_progress_bar=True)
sensitivity_iqrs = calculate_parameter_sensitivity_iqr(sensitivities)
plot_parameter_sensitivity(sensitivities, out_path, lag_window, colors, ALPHA_SORT_NAMES)
# Robustness
df_robust = df[df["Log Source"].isin(["Ceravolo", "Ostovar"])].copy(deep=True)
means_ceravolo = calc_harm_means(df_robust[df_robust["Log Source"] == "Ceravolo"], min_support, lag_window, show_progress_bar=True)
means_ostovar = calc_harm_means(df_robust[df_robust["Log Source"] == "Ostovar"], min_support, lag_window, show_progress_bar=True)
robustness_ceravolo = convert_harm_mean_to_auc(means_ceravolo)
robustness_ostovar = convert_harm_mean_to_auc(means_ostovar)
# Calculate final robustness score by calculating mean of ostovar and ceravolo performance
robustnesses = {
name: (robustness_ceravolo[name] + robustness_ostovar[name]) / 2
for name in robustness_ceravolo.keys()
}
plot_robustness(means_ostovar, means_ceravolo, out_path, lag_window, color_map)
# Save results to csv
results_df = pd.DataFrame([
{
"Algorithm": name,
"Accuracy": accuracies[name],
"Latency": (1-latencies[name])*lag_window,
"Versatility": versatilities[name],
"Scalability": scalabilities[name]["str"],
"Milliseconds per Case": seconds_per_case[name]*1000,
"Milliseconds per Event": seconds_per_event[name]*1000,
"Parameter Sensitivity (IQR)": sensitivity_iqrs[name],
"Robustness": robustnesses[name]
}
for name in df["Algorithm"].unique()
])
results_df.to_csv(f"{out_path}/evaluation_measures{lag_window}.csv", index=False)
def analyze_change_pattern_distribution(df, out_path):
cp_count_results = { cp: dict() for cp in df["Change Pattern"].unique() if cp is not None } # Bose gets lost because it maps to none
for name, group in df[["Log Source", "Log", "Change Pattern"]].drop_duplicates(inplace=False).groupby("Log Source"): # Only consider each logpath once (not once for each algorithm application)
series = group["Change Pattern"].value_counts().to_dict()
for cp, count in series.items():
cp_count_results[cp][name] = count
for key in cp_count_results.keys():
cp_count_results[key]["Bose"] = 0
cp_count_results["Mixed (Bose)"] = {"Ostovar": 0, "Ceravolo": 0, "Bose": 1}
cp_counts = pd.DataFrame(cp_count_results).fillna(0).astype(int)
# Analyze relative counts
cp_counts_tr = cp_counts.transpose()
total_logs = cp_counts.to_numpy().sum()
cp_counts_tr["Relative Frequency"] = cp_counts_tr.apply(lambda row: row.sum() / total_logs * 100, axis=1)
cp_counts_tr.to_csv(f"{out_path}/change_pattern_distribution.csv")
def calcAccuracy(df:pd.DataFrame, param_names:List[str], lag_window: int):
"""Calculates the Accuracy Metric for the given dataframe by grouping by the given parameters and calculating the mean accuracy
Args:
df (pd.DataFrame): The dataframe containing the results to be evaluated
param_names (List[str]): The names of the parameters of this approach
lag_window (int): The lag window to be used for the evaluation to determine true positives and false positives
"""
f1s = dict()
recalls = dict()
precisions = dict()
# Group by parameter values to calculate accuracy per parameter setting, over all logs
for parameters, group in df.groupby(by=param_names):
# Calculate Accuracy for this parameter setting
## --> F1-Score, but first collect all TP and FP
tps = 0
fps = 0
positives = 0
detected = 0
# Collect TP FP, etc.
for index, row in group.iterrows():
actual_cp = row["Actual Changepoints for Log"]
detected_cp = row["Detected Changepoints"]
tp, fp = evaluation.getTP_FP(detected_cp, actual_cp, lag_window)
tps += tp
fps += fp
positives += len(actual_cp)
detected += len(detected_cp)
try:
precisions[parameters] = tps / detected
except ZeroDivisionError:
precisions[parameters] = np.NaN
try:
recalls[parameters] = tps / positives
except ZeroDivisionError:
recalls[parameters] = np.NaN
f1s[parameters] = harmonic_mean([precisions[parameters], recalls[parameters]]) # If either is nan, the harmonic mean is nan
return (precisions, recalls, f1s)
def calculate_accuracy_metric_df(dataframe, lag_window, show_progress_bar: bool = False):
computed_accuracy_dicts = dict()
computed_precision_dicts = dict()
computed_recall_dicts = dict()
accuracy_best_param = dict()
accuracies = dict()
groups = dataframe.groupby(by="Algorithm")
if show_progress_bar:
groups = tqdm(groups, "Calculating Accuracy. Completed Algorithms: ")
for name, a_df in groups:
computed_precision_dicts[name], computed_recall_dicts[name], computed_accuracy_dicts[name] = calcAccuracy(a_df, used_parameters[name], lag_window)
best_param = max(computed_accuracy_dicts[name], key=lambda x: computed_accuracy_dicts[name][x])
accuracy_best_param[name] = best_param
# accuracies[name] = max(computed_accuracy_dicts[name].values())
accuracies[name] = computed_accuracy_dicts[name][best_param]
return (accuracies, computed_accuracy_dicts, computed_precision_dicts, computed_recall_dicts, accuracy_best_param)
def plot_accuracy(computed_accuracy_dicts, computed_precision_dicts, computed_recall_dicts, accuracy_best_param, out_path, lag_window, colors, order):
accuracy_plot_df = pd.DataFrame(
[
{
"Algorithm": name,
"Metric": "F1-Score",
"Value": computed_accuracy_dicts[name][accuracy_best_param[name]]
}
for name in computed_accuracy_dicts.keys()
] + [
{
"Algorithm": name,
"Metric": "Precision",
"Value": computed_precision_dicts[name][accuracy_best_param[name]]
}
for name in computed_accuracy_dicts.keys()
] + [
{
"Algorithm": name,
"Metric": "Recall",
"Value": computed_recall_dicts[name][accuracy_best_param[name]]
}
for name in computed_accuracy_dicts.keys()
]
)
palette=None #{"Precision": "#573deb", "F1-Score": "#ff0076", "Recall": "#ffa600"}
palette = sns.color_palette(colors)
plt.grid(zorder=0)
ax = sns.barplot(x="Metric", y="Value", data=accuracy_plot_df, hue="Algorithm", palette=palette, hue_order=order,zorder =5)
ax.figure.set_size_inches(11, 4)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=18)
ax.set_xlabel(ax.get_xlabel(), size = 20)
ax.set_ylabel("", size = 20)
#ax.get_yaxis().set_visible(False)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0,prop={'size': 14})
if not os.path.exists(f"{out_path}/Accuracy{lag_window}"):
os.makedirs(f"{out_path}/Accuracy{lag_window}")
plt.savefig(f"{out_path}/Accuracy{lag_window}/accuracy_c.pdf", bbox_inches="tight", format="pdf")
ax = sns.pointplot(
x="Algorithm",
y="Value",
data=accuracy_plot_df,
hue="Metric",
order=order,
join=True,
)
ax.figure.set_size_inches(15, 4)
plt.savefig(f"{out_path}/Accuracy{lag_window}/accuracy_points.pdf", bbox_inches="tight", format="pdf")
def calcLatencies(df, param_names, lag_window, show_progress_bar: bool = False):
latencies = dict()
for parameters, group in df.groupby(by=param_names):
lags = []
for index, row in group.iterrows():
actual_cp = row["Actual Changepoints for Log"]
detected_cp = row["Detected Changepoints"]
assignments = evaluation.assign_changepoints(detected_cp, actual_cp, lag_window)
for d, a in assignments:
lags.append(abs(d-a))
latencies[parameters] = lags
return latencies
def calculate_latency(dataframe, lag_window, min_support=1, show_progress_bar: bool = False):
latencies = dict() # Dict holding the best achieved latency per approach
scaled_latency_dicts = dict() # Dict holding the scaled mean latency per approach per parameter setting
computed_latency_dicts = dict() # Dict holding the raw list of detection lags per approach per parameter setting
best_params_latency = dict() # Dict holding the best parameter setting per approach (the one that achieves the best latency)
groups = dataframe.groupby(by="Algorithm")
if show_progress_bar:
groups = tqdm(groups, "Calculating Latency. Completed Algorithms: ")
for name, a_df in groups:
result = calcLatencies(a_df, used_parameters[name], lag_window)
computed_latency_dicts[name] = result
latency_scaled_dict = {
param: 1-(mean(result[param])/lag_window) if len(result[param]) >= min_support else np.NaN
for param in result.keys()
}
scaled_latency_dicts[name] = latency_scaled_dict
best_param = max(latency_scaled_dict, key=lambda x: latency_scaled_dict[x] if not np.isnan(latency_scaled_dict[x]) else -1)
best_params_latency[name] = best_param
latencies[name] = latency_scaled_dict[best_param]
return (latencies, scaled_latency_dicts, computed_latency_dicts, best_params_latency)
def plot_latency(computed_latency_dicts, best_params_latency, colors, out_path, lag_window, order):
latency_plot_df = pd.DataFrame([
{
"Algorithm": name,
"Unscaled Latency": latency
}
for name in computed_latency_dicts.keys()
for latency in computed_latency_dicts[name][best_params_latency[name]]
])
ax = plt.subplots(figsize=(17, 4))
palette = sns.color_palette(colors)
ax = sns.barplot(x="Algorithm", y="Unscaled Latency", data=latency_plot_df,palette= palette, order=order)
plt.ylabel("Latency")# [Traces]")
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
ax.figure.set_size_inches(14, 4)
if not os.path.exists(f"{out_path}/Latency{lag_window}"):
os.makedirs(f"{out_path}/Latency{lag_window}")
plt.savefig(f"{out_path}/Latency{lag_window}/latency.pdf", bbox_inches="tight", format="pdf")
def calc_versatility(dataframe, lag_window, show_progress_bar: bool = False):
versatility_recall_dicts = dict() # Map approach to a dictionary mapping param setting to mean recall over all change patterns
versatilities = dict() # Map approach to versatility score
best_params_versatility = dict() # Map approach to best param setting
groups = dataframe.groupby(by="Algorithm")
if show_progress_bar:
groups = tqdm(groups, "Calculating Versatility. Completed Algorithms: ")
for name, group in groups:
recalls_of_this_approach = dict()
for params, params_group in group.groupby(by=used_parameters[name]):
recalls = dict()
for change_pattern, cp_group in params_group.groupby(by="Change Pattern"):
TPS = 0
POSITIVES = 0
# TP / TP+FN = TP / POSTIVES = Recall
for index, row in cp_group.iterrows():
detected_changepoints = row["Detected Changepoints"]
actual_changepoints = row["Actual Changepoints for Log"]
tp, _ = evaluation.getTP_FP(detected_changepoints, actual_changepoints, lag_window)
TPS += tp
POSITIVES += len(actual_changepoints)
# Recall of this algorithm for this change pattern:
recall = TPS / POSITIVES if POSITIVES != 0 else np.NaN # Only the case if there are no actual changepoints, which should not be the case
recalls[change_pattern] = recall
recalls_of_this_approach[params] = recalls
versatility_recall_dicts[name] = recalls_of_this_approach
best_param = max(recalls_of_this_approach, key=lambda x: np.nanmean(list(recalls_of_this_approach[x].values()))) # .values() gives us all the recalls for all change patterns on this param setting
best_params_versatility[name] = best_param
versatilities[name] = np.nanmean(list(
recalls_of_this_approach[best_param].values()
))
return versatilities, versatility_recall_dicts, best_params_versatility
def plot_versatility(versatility_recall_dicts, best_params_versatility, out_path, lag_window, colors, order):
df_vers_plot = pd.DataFrame([
{
"Algorithm": name,
"Change Pattern": cp,
"Versatility": versatility_recall_dicts[name][best_params_versatility[name]][cp]
}
for name in versatility_recall_dicts.keys()
for cp in versatility_recall_dicts[name][best_params_versatility[name]].keys()
])
fig,ax = plt.subplots(figsize=(20, 4))
sns.barplot(x="Change Pattern", y="Versatility", data=df_vers_plot, hue="Algorithm", ax=ax, hue_order=order)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0)
if not os.path.exists(f"{out_path}/Versatility{lag_window}"):
os.makedirs(f"{out_path}/Versatility{lag_window}")
plt.savefig(f"{out_path}/Versatility{lag_window}/versatility.pdf", bbox_inches="tight", format="pdf")
plt.close('all')
# As points
change_patterns = sorted(list(df_vers_plot["Change Pattern"].unique()))
ax = sns.pointplot(
x="Change Pattern",
y="Versatility",
data=df_vers_plot,
hue="Algorithm",
hue_order=order,
order=change_patterns
)
ax.figure.set_size_inches(20,4)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0)
plt.savefig(f"{out_path}/Versatility{lag_window}/versatility_line_graph.pdf", bbox_inches="tight", format="pdf")
# Plot bars again, but split in half
first_half = change_patterns[:len(change_patterns)//2]
second_half = change_patterns[len(change_patterns)//2:]
fig,ax = plt.subplots(figsize=(20, 4))
sns.barplot(x="Change Pattern", y="Versatility", data=df_vers_plot[df_vers_plot["Change Pattern"].isin(first_half)], hue="Algorithm", order=first_half, ax=ax, hue_order=order)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0)
plt.savefig(f"{out_path}/Versatility{lag_window}/versatility_split_1.pdf", bbox_inches="tight", format="pdf")
fig2,ax2 = plt.subplots(figsize=(20, 4))
sns.barplot(x="Change Pattern", y="Versatility", data=df_vers_plot[df_vers_plot["Change Pattern"].isin(second_half)], hue="Algorithm", order=second_half, ax=ax2, hue_order=order)
plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0)
plt.savefig(f"{out_path}/Versatility{lag_window}/versatility_split_2.pdf", bbox_inches="tight", format="pdf")
# Versatility as Radar Chart
plotly_default_colors = colors#px.colors.qualitative.Plotly
fill_colors = {
name: plotly_default_colors[idx]
for idx,name in enumerate(df_vers_plot["Algorithm"].unique())
}
with warnings.catch_warnings():
warnings.simplefilter(action='ignore', category=FutureWarning)
for alg, dataframe in df_vers_plot.groupby(by="Algorithm"):
fig = px.line_polar(
df_vers_plot[df_vers_plot["Algorithm"] == alg], # Isnt this just dataframe from the groupby...
r='Versatility',
theta='Change Pattern',
line_close=True,
color='Algorithm',
color_discrete_map=fill_colors,
title=None# alg, # Set this to alg if we want the title, I was planning on setting the title in latex
)
fig.update_layout(showlegend=False, title_x=0.5, font=dict(size=18)) # Disable legend (as we have only one algorithm per plot) and center title (if present)
fig.update_traces(fill='toself')
if not os.path.exists(f"{out_path}/Versatility{lag_window}/Radar_Charts"):
os.makedirs(f"{out_path}/Versatility{lag_window}/Radar_Charts")
fig.write_image(f"{out_path}/Versatility{lag_window}/Radar_Charts/radar_chart_{alg.replace(' ','_')}.pdf", format="pdf")
def calculate_scalability(dataframe, show_progress_bar: bool = False):
scalabilities = dict()
groups = dataframe.groupby("Algorithm")
if show_progress_bar:
groups = tqdm(groups, desc="Calculating Scalability. Completed Algorithms: ")
for name, a_df in groups:
result = a_df["Duration (Seconds)"].mean()
result_str = datetime.strftime(datetime.utcfromtimestamp(result), '%H:%M:%S')
scalabilities[name] = {"avg_seconds": result, "str": result_str}
return scalabilities
def plot_scalability(scalabilities, dfs, out_path, lag_window, colors, order):
# Boxplot of Mean Absolute Duration
scalability_plot_df = pd.DataFrame([
{
"Algorithm": name,
"Duration": duration / 60
}
for name, a_df in dfs
for duration in a_df["Duration (Seconds)"].tolist()
])
fig, ax = plt.subplots(figsize=(10, 5))
ax.set_xlabel("Duration [Minutes]")
sorted_names = list(scalabilities.items())
sorted_names.sort(key=lambda x: x[1]["avg_seconds"])
palette = sns.color_palette(colors)
ax = sns.boxplot(
data=scalability_plot_df,
order=order,
x="Duration",
y="Algorithm",
palette = palette,
width=1,
ax=ax,
fliersize=0
)
#ax.figure.set_size_inches(14, 4)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
plt.xlabel("Duration [Minutes]")
if not os.path.exists(f"{out_path}/Scalability{lag_window}"):
os.makedirs(f"{out_path}/Scalability{lag_window}")
plt.savefig(f"{out_path}/Scalability{lag_window}/scalability.pdf", bbox_inches="tight", format="pdf")
# As Barplot
scalability_barplot_df = pd.DataFrame([
{
"Algorithm": name,
"Duration": duration / 60
}
for name, a_df in dfs
for duration in a_df["Duration (Seconds)"].tolist()
])
ax = plt.subplots(figsize=(17, 4))
ax = sns.barplot(x="Algorithm", y="Duration", data=scalability_barplot_df,palette = palette, order=order)
ax.figure.set_size_inches(12, 4)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
plt.ylabel("Duration [Minutes]")
plt.savefig(f"{out_path}/Scalability{lag_window}/scalability_barplot.pdf", bbox_inches="tight", format="pdf")
def calculate_rel_scalabilities(df, loglengths, loglengths_events):
df_seconds = df.copy()
# Add Log Length Column
df_seconds["Log Length (Cases)"] = df_seconds[["Log Source", "Log"]].apply(axis=1, func=lambda x: loglengths[Path("EvaluationLogs", x[0], x[1]+".xes.gz")])
df_seconds["Log Length (Events)"] = df_seconds[["Log Source", "Log"]].apply(axis=1, func=lambda x: loglengths_events[Path("EvaluationLogs", x[0], x[1]+".xes.gz")])
seconds_per_case = dict()
seconds_per_event = dict()
for name, group in df_seconds.groupby("Algorithm"):
total_seconds = group["Duration (Seconds)"].sum()
total_cases = group["Log Length (Cases)"].sum()
total_events = group["Log Length (Events)"].sum()
seconds_per_case[name] = total_seconds / total_cases
seconds_per_event[name] = total_seconds / total_events
df_seconds["Milliseconds per Event"] = df_seconds.apply(lambda x: (x["Duration (Seconds)"]*1000) / x["Log Length (Events)"], axis=1)
return df_seconds, seconds_per_case, seconds_per_event
def plot_rel_scalability(df_seconds, out_path, lag_window, colors, order):
fig, ax = plt.subplots(figsize=(10, 5))
ax.set_xlabel("Milliseconds per Event")
palette = sns.color_palette(colors)
ax = sns.boxplot(
data=df_seconds,
order=order,
x="Milliseconds per Event",
y="Algorithm",
palette = palette,
width=1,
ax=ax,
fliersize=0
)
ax.set_xscale("log")
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
plt.xlabel("Milliseconds per Event")
if not os.path.exists(f"{out_path}/Scalability{lag_window}"):
os.makedirs(f"{out_path}/Scalability{lag_window}")
plt.savefig(f"{out_path}/Scalability{lag_window}/scalability_mseconds_per_event.pdf", bbox_inches="tight", format="pdf")
ax = plt.subplots(figsize=(17, 4))
ax = sns.barplot(x="Algorithm", y="Milliseconds per Event", data=df_seconds, palette = palette, order=order)
ax.figure.set_size_inches(12, 4)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
ax.set_yscale("log")
plt.ylabel("Milliseconds per Event")
if not os.path.exists(f"{out_path}/Scalability{lag_window}"):
os.makedirs(f"{out_path}/Scalability{lag_window}")
plt.savefig(f"{out_path}/Scalability{lag_window}/scalability_mseconds_per_event_barplot.pdf", bbox_inches="tight", format="pdf")
def calculate_parameter_sensitivity(df, versatility_recall_dicts, computed_accuracy_dicts, scaled_latency_dicts, show_progress_bar: bool = False):
versatility_score_per_param = {
name: {
params: np.nanmean(list(versatility_recall_dicts[name][params].values()))
for params in versatility_recall_dicts[name].keys()
}
for name in versatility_recall_dicts.keys()
}
algnames = df["Algorithm"].unique()
if show_progress_bar:
algnames = tqdm(algnames, "Calculating Parameter Sensitivity. Completed Algorithms: ")
sensitivities = dict()
for name in algnames:
_sensitivities = dict()
acc = computed_accuracy_dicts[name]
lat = scaled_latency_dicts[name]
vers = versatility_score_per_param[name]
for param_choice in acc.keys():
sensitivity = harmonic_mean([acc[param_choice], vers[param_choice], lat[param_choice]])
_sensitivities[param_choice] = sensitivity
sensitivities[name] = _sensitivities
return sensitivities
def plot_parameter_sensitivity(sensitivities, out_path, lag_window, colors, order):
sens_df = pd.DataFrame([
{
"Parameters": param,
"Algorithm": name,
"Sensitivity Score": sens
}
for name, sens_dict in sensitivities.items()
for param, sens in sens_dict.items()
])
palette = sns.color_palette(colors)
fig, ax = plt.subplots(figsize=(10, 5))
ax = sns.boxplot(
data=sens_df,
x="Sensitivity Score",
y="Algorithm",
palette = palette,
ax=ax,
fliersize=0,
order=order
)
ax.figure.set_size_inches(7, 4)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel("Performance", size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
if not os.path.exists(f"{out_path}/Parameter_Sensitivity{lag_window}"):
os.makedirs(f"{out_path}/Parameter_Sensitivity{lag_window}")
plt.savefig(f"{out_path}/Parameter_Sensitivity{lag_window}/param_sensitivity.pdf", bbox_inches="tight", format="pdf")
def calculate_parameter_sensitivity_iqr(sensitivities):
sensitivity_iqrs = dict()
for name, sens in sensitivities.items():
_iqr = iqr(list(sens.values()), nan_policy="omit")
sensitivity_iqrs[name] = _iqr
return sensitivity_iqrs
def calc_harm_means(dataframe, min_support, lag_window, show_progress_bar: bool = False):
robustnesses = dict()
groups = dataframe.groupby("Noise Level")
if show_progress_bar:
groups = tqdm(groups, "Calculating Harmonic Means for Robustness. Noise Levels Completed:")
for noise_level, noise_df in groups:
# Calculate accuracy over these logs
accuracies, _, _, _, _ = calculate_accuracy_metric_df(noise_df, lag_window, show_progress_bar=False)
latencies, _, _, _ = calculate_latency(noise_df, lag_window, min_support=min_support, show_progress_bar=False)
versatilities, _, _ = calc_versatility(noise_df, lag_window, show_progress_bar=False)
assert accuracies.keys() == latencies.keys() == versatilities.keys()
robustnesses[noise_level] = {
approach: harmonic_mean([accuracies[approach], latencies[approach], versatilities[approach]])
for approach in accuracies.keys()
}
return robustnesses
def convert_harm_mean_to_auc(means, div_zero_default=0):
## Conventions:
# If any harmonic mean is nan, then set it to 0 instead
def _convert_nan_to_zero(x):
if np.isnan(x):
x = 0
return x
# means maps {noise_level: {approach: robustness}}
# reformat to {approach: [(noise_level, robustness)]}
means = {
approach: [
(int(noise_level), _convert_nan_to_zero(means[noise_level][approach]))
for noise_level in means.keys()
]
for approach in means["0"].keys()
}
robustnesses = dict()
for approach in means.keys():
points = sorted(means[approach], key=lambda x: x[0])
# Initial Robustness - Use to model "Ideal Robustness"
initial_robustness = points[0][1] # Robustness for lowest (0%) noise
ideal_auc = initial_robustness * (points[-1][0] - points[0][0]) # AUC if it was always exactly as good as for the lowest noise level. If performance increases with higher noise, the achieved AUC might be larger than ideal AUC
prev_noise, prev_robust = points[0]
auc = 0
for noise, robust in points[1:]:
delta_noise = noise-prev_noise
sum_robust = prev_robust+robust
# "ROC Index"
area = (delta_noise * sum_robust) / 2# Area under curve for this segment
auc += area
prev_noise = noise
prev_robust = robust
if ideal_auc != 0:
robustnesses[approach] = auc / ideal_auc
else:
robustnesses[approach] = div_zero_default
return robustnesses
def _plot_robustness_one(robustness_df, logset, color_map):
fig, axs = plt.subplots(2,4, figsize=(12,6))
#fig.suptitle(f"Robustness of Approaches for {logset} Logs")
for idx, name in enumerate(robustness_df["Approach"].unique()):
row_offset = idx // 4
col_offset = idx % 4
ax = axs[row_offset, col_offset]
relevant_df = robustness_df[(robustness_df["Approach"] == name) & (robustness_df["Log Set"] == logset)]
sns.pointplot(x="Noise Level", y="Robustness", data=relevant_df, errorbar=None,color= color_map[name], ax=ax)#, title=name)
ax.set_ylim(0,1)
ax.set_title(name, size = 16)
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel() if row_offset == 1 else "", size = 18)
ax.set_ylabel("Performance" if col_offset == 0 else "", size = 18)
robustness_of_zero_noise = min(zip(relevant_df["Noise Level"], relevant_df["Robustness"]), key=lambda x: x[0])[1]
if np.isnan(robustness_of_zero_noise):
robustness_of_zero_noise = 0
# plt.axhline(y=robustness_of_zero_noise, color='r', linestyle='-', ax=ax)
ax.hlines(robustness_of_zero_noise, color=color_map[name], linestyle='dashed', xmin=0, xmax=len(relevant_df["Noise Level"].unique())-1)
fig.tight_layout()
#ax.grid()
fig.set_facecolor('0.9')
return fig, axs
def plot_robustness(means_ostovar, means_ceravolo, out_path, lag_window, color_map):
plot_df_robust = pd.DataFrame(
([
{
"Log Set": "Ostovar",
"Approach": name,
"Noise Level": int(noise_level),
"Robustness": robustness
}
for noise_level, robust_dict in means_ostovar.items()
for name, robustness in robust_dict.items()
] + [
{
"Log Set": "Ceravolo",
"Approach": name,
"Noise Level": int(noise_level),
"Robustness": robustness
}
for noise_level, robust_dict in means_ceravolo.items()
for name, robustness in robust_dict.items()
])
)
if not os.path.exists(f"{out_path}/Robustness{lag_window}"):
os.makedirs(f"{out_path}/Robustness{lag_window}")
fig_ostovar, axs_ostovar = _plot_robustness_one(plot_df_robust, "Ostovar", color_map)
fig_ostovar.savefig(f"{out_path}/Robustness{lag_window}/robustness_ostovar_logs.pdf", bbox_inches="tight", format="pdf")
fig_ceravolo, axs_ceravolo = _plot_robustness_one(plot_df_robust, "Ceravolo", color_map)
fig_ceravolo.savefig(f"{out_path}/Robustness{lag_window}/robustness_ceravolo_logs.pdf", bbox_inches="tight", format="pdf")
plt.close('all')
fig_ostovar_in_one = _plot_robustness_in_one(plot_df_robust, "Ostovar", color_map)
fig_ostovar_in_one.savefig(f"{out_path}/Robustness{lag_window}/robustness_ostovar_logs_all_in_one.pdf", bbox_inches="tight", format="pdf")
fig_ceravolo_in_one = _plot_robustness_in_one(plot_df_robust, "Ceravolo", color_map)
fig_ceravolo_in_one.savefig(f"{out_path}/Robustness{lag_window}/robustness_ceravolo_logs_all_in_one.pdf", bbox_inches="tight", format="pdf")
def _plot_robustness_in_one(robustness_df, logset, color_map):
for _, name in enumerate(robustness_df["Approach"].unique()):
relevant_df = robustness_df[(robustness_df["Approach"] == name) & (robustness_df["Log Set"] == logset)]
ax = sns.pointplot(x="Noise Level", y="Robustness", data=relevant_df, errorbar=None,color= color_map[name])#, title=name)
ax.set_ylim(0,1)
ax.figure.set_size_inches(5, 12)
#ax.set_title(logset+" Logs with Different Noise Levels", size = 18)
ax.set_xlabel("Noise (%)")
ax.spines.right.set_visible(False)
ax.spines.top.set_visible(False)
ax.tick_params(labelsize=16)
ax.set_xlabel(ax.get_xlabel(), size = 18)
ax.set_ylabel(ax.get_ylabel(), size = 18)
robustness_of_zero_noise = min(zip(relevant_df["Noise Level"], relevant_df["Robustness"]), key=lambda x: x[0])[1]
if np.isnan(robustness_of_zero_noise):
robustness_of_zero_noise = 0
# plt.axhline(y=robustness_of_zero_noise, color='r', linestyle='-', ax=ax)
ax.hlines(robustness_of_zero_noise, color=color_map[name], linestyle='dashed', xmin=0, xmax=len(relevant_df["Noise Level"].unique())-1)
return ax.figure