From 65eb84057000642ef63f6ce20ecf7aa0254b1e4d Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 3 Dec 2024 11:44:06 +0100 Subject: [PATCH 01/20] added empty endpoint for future time series analysis approaches Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 6 +++++ .../metadata_analyzer/time_series_analyzer.py | 27 +++++++++++++++++++ 2 files changed, 33 insertions(+) create mode 100644 apps/analyzer/metadata_analyzer/time_series_analyzer.py diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 6877d26..331e662 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -4,6 +4,7 @@ from metadata_analyzer.simple_analyzer import SimpleAnalyzer from metadata_analyzer.simple_rule_based_analyzer import SimpleRuleBasedAnalyzer from metadata_analyzer.analyzer import Analyzer +from metadata_analyzer.time_series_analyzer import Time_series_analyzer from metadata_analyzer.backend import Backend from flasgger import Swagger import requests @@ -161,6 +162,11 @@ def simple_rule_based_analysis_inc(): alert_limit = json["alertLimit"] return jsonify(Analyzer.simple_rule_based_analysis_inc(alert_limit)) +@app.route("/timeSeriesTests", methods=["GET"]) +def runTimeSeriesTests(): + result = Time_series_analyzer.analyze_basic() + return 'Time series analysis not yet supported', 501 + def main(): database = Database() backend = Backend(os.getenv("BACKEND_URL")) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py new file mode 100644 index 0000000..fa87b2b --- /dev/null +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -0,0 +1,27 @@ +import pandas as pd +from darts import TimeSeries +from sqlalchemy import create_engine +from metadata_analyzer.database import Database + + +class Time_series_analyzer: + + def init(): + # Create an engine using shared init + database = Database() + + # read table into a dataframe + with database.engine.connect() as conn, conn.begin(): + data_frame = pd.read_sql_table("results", conn) + + # preprocessing + # removes non-backups + data_frame.drop(data_frame[data_frame['is_backup'] == 0].index) + # removes non-full backups (also removes copy backups in dataset for now) + data_frame.drop(data_frame[data_frame['fdi_type'] == 'D'].index) + data_frame.drop(data_frame[data_frame['fdi_type'] == 'I'].index) + data_frame.drop(data_frame[data_frame['fdi_type'] == 'C'].index) + + def analyze_basic(): + # Time series analysis here + return -1 \ No newline at end of file From 517ea13e89edb23af4912e64a8e050f0bb50ad83 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 3 Dec 2024 11:51:23 +0100 Subject: [PATCH 02/20] framework for future time series tests added Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 28 +++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 apps/analyzer/metadata_analyzer/timeSeriesTests.py diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py new file mode 100644 index 0000000..02ddb19 --- /dev/null +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -0,0 +1,28 @@ +import pandas as pd +from darts import TimeSeries +from sqlalchemy import create_engine +from metadata_analyzer.database import Database + + + +# Create an engine using shared init +database = Database() + +# read table into a dataframe +with database.engine.connect() as conn, conn.begin(): + data_frame = pd.read_sql_table("results", conn) + +# preprocessing +# removes non-backups +data_frame.drop(data_frame[data_frame['is_backup'] == 0].index) +# removes non-full backups (also removes copy backups in dataset for now) +data_frame.drop(data_frame[data_frame['fdi_type'] == 'D'].index) +data_frame.drop(data_frame[data_frame['fdi_type'] == 'I'].index) +data_frame.drop(data_frame[data_frame['fdi_type'] == 'C'].index) + + + + + +series = TimeSeries.from_dataframe(data_frame, 'sbc_start', 'data_size',fill_missing_dates=True,freq='None') + \ No newline at end of file From 20c4709fdb67e32ed38e4760ded474a3728f24c8 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 3 Dec 2024 13:31:26 +0100 Subject: [PATCH 03/20] fixed filtering of values and poetry setup Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 21 +- .../metadata_analyzer/time_series_analyzer.py | 14 +- apps/analyzer/poetry.lock | 2602 ++++++++++++++++- apps/analyzer/pyproject.toml | 1 + 4 files changed, 2587 insertions(+), 51 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py index 02ddb19..f7f26ad 100644 --- a/apps/analyzer/metadata_analyzer/timeSeriesTests.py +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -2,6 +2,7 @@ from darts import TimeSeries from sqlalchemy import create_engine from metadata_analyzer.database import Database +from collections import Counter @@ -13,16 +14,20 @@ data_frame = pd.read_sql_table("results", conn) # preprocessing +# removes null values in sbc_start and is_backup +data_frame = data_frame[data_frame.sbc_start.notnull()] +data_frame = data_frame[data_frame.is_backup.notnull()] # removes non-backups -data_frame.drop(data_frame[data_frame['is_backup'] == 0].index) +data_frame = data_frame[data_frame.is_backup != 0] # removes non-full backups (also removes copy backups in dataset for now) -data_frame.drop(data_frame[data_frame['fdi_type'] == 'D'].index) -data_frame.drop(data_frame[data_frame['fdi_type'] == 'I'].index) -data_frame.drop(data_frame[data_frame['fdi_type'] == 'C'].index) +# redo with only == 'F' +data_frame = data_frame[data_frame.fdi_type != 'D'] +data_frame = data_frame[data_frame.fdi_type != 'I'] +data_frame = data_frame[data_frame.fdi_type != 'C'] +# remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) +data_frame = data_frame.drop_duplicates(subset=['sbc_start']) - - - -series = TimeSeries.from_dataframe(data_frame, 'sbc_start', 'data_size',fill_missing_dates=True,freq='None') + +series = TimeSeries.from_dataframe(data_frame, 'sbc_start', 'data_size',fill_missing_dates=True,freq='14s') \ No newline at end of file diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index fa87b2b..75c61e5 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -15,12 +15,18 @@ def init(): data_frame = pd.read_sql_table("results", conn) # preprocessing + # removes null values in sbc_start and is_backup + data_frame = data_frame[data_frame.sbc_start.notnull()] + data_frame = data_frame[data_frame.is_backup.notnull()] # removes non-backups - data_frame.drop(data_frame[data_frame['is_backup'] == 0].index) + data_frame = data_frame[data_frame.is_backup != 0] # removes non-full backups (also removes copy backups in dataset for now) - data_frame.drop(data_frame[data_frame['fdi_type'] == 'D'].index) - data_frame.drop(data_frame[data_frame['fdi_type'] == 'I'].index) - data_frame.drop(data_frame[data_frame['fdi_type'] == 'C'].index) + # redo with only == 'F' + data_frame = data_frame[data_frame.fdi_type != 'D'] + data_frame = data_frame[data_frame.fdi_type != 'I'] + data_frame = data_frame[data_frame.fdi_type != 'C'] + # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) + data_frame = data_frame.drop_duplicates(subset=['sbc_start']) def analyze_basic(): # Time series analysis here diff --git a/apps/analyzer/poetry.lock b/apps/analyzer/poetry.lock index e66d351..48f9179 100644 --- a/apps/analyzer/poetry.lock +++ b/apps/analyzer/poetry.lock @@ -1,5 +1,153 @@ # This file 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refactoring Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 19 ++++++++++--------- .../metadata_analyzer/time_series_analyzer.py | 16 ++++++++-------- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py index f7f26ad..2789bdc 100644 --- a/apps/analyzer/metadata_analyzer/timeSeriesTests.py +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -11,23 +11,24 @@ # read table into a dataframe with database.engine.connect() as conn, conn.begin(): - data_frame = pd.read_sql_table("results", conn) + df = pd.read_sql_table("results", conn) # preprocessing # removes null values in sbc_start and is_backup -data_frame = data_frame[data_frame.sbc_start.notnull()] -data_frame = data_frame[data_frame.is_backup.notnull()] +df = df[df.sbc_start.notnull()] +df = df[df.is_backup.notnull()] # removes non-backups -data_frame = data_frame[data_frame.is_backup != 0] +df = df[df.is_backup != 0] # removes non-full backups (also removes copy backups in dataset for now) # redo with only == 'F' -data_frame = data_frame[data_frame.fdi_type != 'D'] -data_frame = data_frame[data_frame.fdi_type != 'I'] -data_frame = data_frame[data_frame.fdi_type != 'C'] +df = df[df.fdi_type != 'D'] +df = df[df.fdi_type != 'I'] +df = df[df.fdi_type != 'C'] # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) -data_frame = data_frame.drop_duplicates(subset=['sbc_start']) +df = df.drop_duplicates(subset=['sbc_start']) -series = TimeSeries.from_dataframe(data_frame, 'sbc_start', 'data_size',fill_missing_dates=True,freq='14s') + +series = TimeSeries.from_dataframe(df, 'sbc_start', 'data_size',fill_missing_dates=True,freq='D') \ No newline at end of file diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 75c61e5..8170db5 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -12,21 +12,21 @@ def init(): # read table into a dataframe with database.engine.connect() as conn, conn.begin(): - data_frame = pd.read_sql_table("results", conn) + df = pd.read_sql_table("results", conn) # preprocessing # removes null values in sbc_start and is_backup - data_frame = data_frame[data_frame.sbc_start.notnull()] - data_frame = data_frame[data_frame.is_backup.notnull()] + df = df[df.sbc_start.notnull()] + df = df[df.is_backup.notnull()] # removes non-backups - data_frame = data_frame[data_frame.is_backup != 0] + df = df[df.is_backup != 0] # removes non-full backups (also removes copy backups in dataset for now) # redo with only == 'F' - data_frame = data_frame[data_frame.fdi_type != 'D'] - data_frame = data_frame[data_frame.fdi_type != 'I'] - data_frame = data_frame[data_frame.fdi_type != 'C'] + df = df[df.fdi_type != 'D'] + df = df[df.fdi_type != 'I'] + df = df[df.fdi_type != 'C'] # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) - data_frame = data_frame.drop_duplicates(subset=['sbc_start']) + df = df.drop_duplicates(subset=['sbc_start']) def analyze_basic(): # Time series analysis here From eeb67417c7de6bd877390f7d070e12479589dd07 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 3 Dec 2024 17:33:37 +0100 Subject: [PATCH 05/20] working preprocessing and cleanup Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 20 +++++++++++-------- .../metadata_analyzer/time_series_analyzer.py | 14 +------------ 2 files changed, 13 insertions(+), 21 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py index 2789bdc..0f631f3 100644 --- a/apps/analyzer/metadata_analyzer/timeSeriesTests.py +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -2,8 +2,6 @@ from darts import TimeSeries from sqlalchemy import create_engine from metadata_analyzer.database import Database -from collections import Counter - # Create an engine using shared init @@ -19,16 +17,22 @@ df = df[df.is_backup.notnull()] # removes non-backups df = df[df.is_backup != 0] +#removes backups with size of 0.0 +df = df[df.data_size != 0.0] # removes non-full backups (also removes copy backups in dataset for now) -# redo with only == 'F' -df = df[df.fdi_type != 'D'] -df = df[df.fdi_type != 'I'] -df = df[df.fdi_type != 'C'] +df = df[df.fdi_type == 'F'] # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) df = df.drop_duplicates(subset=['sbc_start']) +# sorts dataframe by sbc_start +df = df.sort_values('sbc_start') +# removes columns that are not relevant (change for other analyses) +df = df[['sbc_start','data_size']] +df.index = df['sbc_start'] # sets index to datetime in sbc_start column +df = df.drop('sbc_start',axis=1) # removes column because values are now in index +# resamples series to emulate backups at regular intervals, alternative: #new_df = df.resample('d').mean() +df = df.asfreq('1D', method='pad') -series = TimeSeries.from_dataframe(df, 'sbc_start', 'data_size',fill_missing_dates=True,freq='D') - \ No newline at end of file +series = TimeSeries.from_series(df,fill_missing_dates=True,freq=None) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 8170db5..93dcc4f 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -14,19 +14,7 @@ def init(): with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) - # preprocessing - # removes null values in sbc_start and is_backup - df = df[df.sbc_start.notnull()] - df = df[df.is_backup.notnull()] - # removes non-backups - df = df[df.is_backup != 0] - # removes non-full backups (also removes copy backups in dataset for now) - # redo with only == 'F' - df = df[df.fdi_type != 'D'] - df = df[df.fdi_type != 'I'] - df = df[df.fdi_type != 'C'] - # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) - df = df.drop_duplicates(subset=['sbc_start']) + # do preprocessing def analyze_basic(): # Time series analysis here From 1514acbd58b357b1f18cdbbd7906922d651e0dcc Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Wed, 4 Dec 2024 12:22:25 +0100 Subject: [PATCH 06/20] improved preprocessing as well as reducing interpolation Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py index 0f631f3..4aafbeb 100644 --- a/apps/analyzer/metadata_analyzer/timeSeriesTests.py +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -11,10 +11,11 @@ with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) -# preprocessing -# removes null values in sbc_start and is_backup +#--------------- General Preprocessing --------------- +# removes null values in sbc_start, task_uuid and is_backup df = df[df.sbc_start.notnull()] df = df[df.is_backup.notnull()] +df = df[df.task_uuid.notnull()] # removes non-backups df = df[df.is_backup != 0] #removes backups with size of 0.0 @@ -25,14 +26,20 @@ df = df.drop_duplicates(subset=['sbc_start']) # sorts dataframe by sbc_start df = df.sort_values('sbc_start') +# gets all possible values for task_uuid +task_ids = df['task_uuid'].unique() +#--------------- Task Specific Preprocessing ---------------# +# removes backups that do not have chosen task id; current task has frequency of 86402s +df = df[df.task_uuid == task_ids[0]] # removes columns that are not relevant (change for other analyses) df = df[['sbc_start','data_size']] + df.index = df['sbc_start'] # sets index to datetime in sbc_start column df = df.drop('sbc_start',axis=1) # removes column because values are now in index -# resamples series to emulate backups at regular intervals, alternative: #new_df = df.resample('d').mean() -df = df.asfreq('1D', method='pad') +# interpolates series to emulate backups at regular intervals, different methods possible +df = df.asfreq('86402s', method='ffill') -series = TimeSeries.from_series(df,fill_missing_dates=True,freq=None) +series = TimeSeries.from_series(df,fill_missing_dates=False,freq='86402s') From 466b40eac0a2ebcb77445ae75506d31b13bfef6e Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Mon, 9 Dec 2024 16:21:00 +0100 Subject: [PATCH 07/20] initial rework of time series control class Signed-off-by: Amelie Regl --- .../metadata_analyzer/time_series_analyzer.py | 40 ++++++++++++++++--- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 93dcc4f..d638332 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -2,11 +2,13 @@ from darts import TimeSeries from sqlalchemy import create_engine from metadata_analyzer.database import Database +from flask import jsonify class Time_series_analyzer: + df = "" - def init(): + def init(self): # Create an engine using shared init database = Database() @@ -14,8 +16,36 @@ def init(): with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) - # do preprocessing - - def analyze_basic(): + # --------------- General Preprocessing --------------- + # removes null values in sbc_start, task_uuid and is_backup + df = df[df.sbc_start.notnull()] + df = df[df.is_backup.notnull()] + df = df[df.task_uuid.notnull()] + df = df[df.data_size.notnull()] + # removes non-backups + df = df[df.is_backup != 0] + # removes backups with size of 0.0 + df = df[df.data_size != 0.0] + # removes non-full backups (also removes copy backups in dataset for now) + df = df[df.fdi_type == "F"] + # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) + df = df.drop_duplicates(subset=["sbc_start"]) + # sorts dataframe by sbc_start + df = df.sort_values("sbc_start") + + def analyze_basic(self): # Time series analysis here - return -1 \ No newline at end of file + return -1 + + def get_task_ids(self): + # gets all possible values for task_uuid + task_ids = self.df["task_uuid"].unique() + return jsonify(task_ids.to_dict()) + + def get_frequencies(self): + # gets backup frequencies present in dataset + freqs = self.df.diff()["sbc_start"] + # orders frequencies by count and removes NaN edge cases + freqs = freqs.value_counts() + freqs = freqs[freqs.notnull()] + return jsonify(freqs.to_dict()) From eb76080940a9f8d6694a84ca55fe039a4a15365e Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Mon, 9 Dec 2024 23:10:13 +0100 Subject: [PATCH 08/20] built basic working anomaly detection using time series and real data Signed-off-by: Amelie Regl --- .../metadata_analyzer/timeSeriesTests.py | 78 +++++++++++++++---- 1 file changed, 63 insertions(+), 15 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/timeSeriesTests.py b/apps/analyzer/metadata_analyzer/timeSeriesTests.py index 4aafbeb..22eb413 100644 --- a/apps/analyzer/metadata_analyzer/timeSeriesTests.py +++ b/apps/analyzer/metadata_analyzer/timeSeriesTests.py @@ -1,7 +1,13 @@ +# %% import pandas as pd from darts import TimeSeries +from darts.ad import ( + KMeansScorer, +) +from darts.ad.detectors import QuantileDetector from sqlalchemy import create_engine from metadata_analyzer.database import Database +from flask import jsonify # Create an engine using shared init @@ -11,35 +17,77 @@ with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) -#--------------- General Preprocessing --------------- +# --------------- General Preprocessing --------------- # removes null values in sbc_start, task_uuid and is_backup df = df[df.sbc_start.notnull()] df = df[df.is_backup.notnull()] df = df[df.task_uuid.notnull()] +df = df[df.data_size.notnull()] # removes non-backups df = df[df.is_backup != 0] -#removes backups with size of 0.0 +# removes backups with size of 0.0 df = df[df.data_size != 0.0] # removes non-full backups (also removes copy backups in dataset for now) -df = df[df.fdi_type == 'F'] +df = df[df.fdi_type == "F"] # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) -df = df.drop_duplicates(subset=['sbc_start']) +df = df.drop_duplicates(subset=["sbc_start"]) # sorts dataframe by sbc_start -df = df.sort_values('sbc_start') -# gets all possible values for task_uuid -task_ids = df['task_uuid'].unique() +df = df.sort_values("sbc_start") +# gets all possible values for task_uuid and data_size (debugging utility) +task_ids = df["task_uuid"].unique() +sizes = df["data_size"].unique() + +# --------------- Task Specific Preprocessing ---------------# +# removes backups that do not have chosen task id; current task has frequency of 86401s +df = df[df.task_uuid == task_ids[6]] -#--------------- Task Specific Preprocessing ---------------# -# removes backups that do not have chosen task id; current task has frequency of 86402s -df = df[df.task_uuid == task_ids[0]] # removes columns that are not relevant (change for other analyses) -df = df[['sbc_start','data_size']] +df = df[["sbc_start", "data_size"]] + +# init if utility variable for looking at frequencies +freqs = df.sort_values("sbc_start") +# frequencies present in dataset +freqs = df.diff()["sbc_start"] +freqs = freqs.value_counts() +freqs = freqs[freqs.notnull()] -df.index = df['sbc_start'] # sets index to datetime in sbc_start column -df = df.drop('sbc_start',axis=1) # removes column because values are now in index +df.index = df["sbc_start"] # sets index to datetime in sbc_start column +df = df.drop("sbc_start", axis=1) # removes column because values are now in index # interpolates series to emulate backups at regular intervals, different methods possible -df = df.asfreq('86402s', method='ffill') +df = df.asfreq("86401s", method="ffill") +# df.plot() + +# utility variable for sizes after reduced to one task +sizes = df["data_size"].unique() +clusters = len(sizes) + +# init actual series +series = TimeSeries.from_series(df, fill_missing_dates=False, freq="86401s") + +# interim definition of training data, change to something more useful +series_train = series[:100] + +# using basic k-means scorer (moving window comparison) +Kmeans_scorer = KMeansScorer(k=2, window=1, component_wise=False) +Kmeans_scorer.fit(series_train) +# computed anomaly scores +anomaly_score = Kmeans_scorer.score(series) + +# detects where anomalies lie, then return binary prediction +threshold = 0.95 +detector = QuantileDetector(high_quantile=threshold) +anomaly_pred = detector.fit_detect(series=anomaly_score) + +# TODO decide on interface to backend and return useful values in useful format +anomaly_timestamps = [] +vals = anomaly_pred.all_values() +# gets timestamps of anomalies +for i in range(len(vals)): + if vals[i] == 1.0: + anomaly_timestamps.append(series.get_timestamp_at_point(i)) +print("timestamps of detected anomalies:") +print(anomaly_timestamps) -series = TimeSeries.from_series(df,fill_missing_dates=False,freq='86402s') +# %% From c7ce7fdaef7bebc4c7b71ae0df211de0744edf4f Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Mon, 9 Dec 2024 23:11:30 +0100 Subject: [PATCH 09/20] implemented interface and logic for first time series anomaly detection Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 58 +++++++++++- .../metadata_analyzer/time_series_analyzer.py | 92 ++++++++++++++++--- 2 files changed, 134 insertions(+), 16 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 331e662..65d14db 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -14,6 +14,7 @@ swagger = Swagger(app) load_dotenv(dotenv_path=".env") + @app.route("/") def hello_world(): """Most basic example endpoint that returns a hello world message. @@ -111,6 +112,7 @@ def analyze(): """ return jsonify(Analyzer.analyze()) + @app.route("/updateBasicBackupData", methods=["POST"]) def update_data(): """Updates the backend database with values taken from the analyzer database. @@ -144,33 +146,83 @@ def update_data(): """ return jsonify(Analyzer.update_data()) + @app.route("/simpleRuleBasedAnalysis", methods=["POST"]) def simple_rule_based_analysis(): json = request.get_json() alert_limit = json["alertLimit"] return jsonify(Analyzer.simple_rule_based_analysis(alert_limit)) + @app.route("/simpleRuleBasedAnalysisDiff", methods=["POST"]) def simple_rule_based_analysis_diff(): json = request.get_json() alert_limit = json["alertLimit"] return jsonify(Analyzer.simple_rule_based_analysis_diff(alert_limit)) + @app.route("/simpleRuleBasedAnalysisInc", methods=["POST"]) def simple_rule_based_analysis_inc(): json = request.get_json() alert_limit = json["alertLimit"] return jsonify(Analyzer.simple_rule_based_analysis_inc(alert_limit)) -@app.route("/timeSeriesTests", methods=["GET"]) + +# TODO yaml for swagger +@app.route("/kMeansAnomalies", methods=["POST"]) def runTimeSeriesTests(): - result = Time_series_analyzer.analyze_basic() - return 'Time series analysis not yet supported', 501 + json = request.get_json() + field = "None" + try: + field = "variable" + variable = json["variable"] + field = "task_id" + task_id = json["task_id"] + field = "frequency" + frequency = json["frequency"] + field = "backup_type" + backup_type = json["backup_type"] + field = "window_size" + window_size = json["window_size"] + except KeyError: + return "Missing field of type " + field + + try: + result = Time_series_analyzer.k_means_analyze( + variable, task_id, frequency, backup_type, window_size + ) + return jsonify(result) + except Exception as ex: + return "Misc error occurred!", 500 + + +@app.route("/getTaskIds", methods=["GET"]) +def return_task_ids(): + return jsonify(Time_series_analyzer.get_task_ids()) + + +@app.route("/getFrequenciesForTask", methods=["POST"]) +def return_frequencies(): + json = request.get_json() + field = "None" + try: + field = "task_id" + task_id = json["task_id"] + field = "backup_type" + backup_type = json["backup_type"] + field = "variable" + variable = json["variable"] + except KeyError: + return "Missing field of type " + field + + return jsonify(Time_series_analyzer.get_frequencies(task_id, backup_type, variable)) + def main(): database = Database() backend = Backend(os.getenv("BACKEND_URL")) simple_analyzer = SimpleAnalyzer() + time_series_analyzer = Time_series_analyzer() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) Analyzer.init(database, backend, simple_analyzer, simple_rule_based_analyzer) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index d638332..7163b53 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -3,15 +3,18 @@ from sqlalchemy import create_engine from metadata_analyzer.database import Database from flask import jsonify +import numpy as np +from darts.ad import KMeansScorer +from darts.ad.detectors import QuantileDetector class Time_series_analyzer: df = "" - def init(self): + def __init__(self): + global df # Create an engine using shared init database = Database() - # read table into a dataframe with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) @@ -26,26 +29,89 @@ def init(self): df = df[df.is_backup != 0] # removes backups with size of 0.0 df = df[df.data_size != 0.0] - # removes non-full backups (also removes copy backups in dataset for now) - df = df[df.fdi_type == "F"] # remove entries that have the same sbc_start, necessary for indexing the time axis (could cause problems later) df = df.drop_duplicates(subset=["sbc_start"]) # sorts dataframe by sbc_start df = df.sort_values("sbc_start") - def analyze_basic(self): - # Time series analysis here - return -1 + def k_means_analyze(variable, task_id, frequency, backup_type, window_size): + global df + + Time_series_analyzer.task_preprocessing(backup_type, task_id, variable) + + df.index = df["sbc_start"] # sets index to datetime in sbc_start column + df = df.drop( + "sbc_start", axis=1 + ) # removes column because values are now in index + + # interpolates series to emulate backups at regular intervals, different methods possible + frequency = frequency + "s" # adds unit + df = df.asfreq(frequency, method="ffill") + # determines number of clusters for the k means scorer, + # TODO use clusters for more useful k value + clusters = len(df[variable].unique()) + # initializes time series + series = TimeSeries.from_series(df, fill_missing_dates=False, freq=frequency) + + # TODO interim definition of training data, change to something more useful + series_train = series[: round(len(series) / 4)] + + # using basic k-means scorer (moving window comparison) + Kmeans_scorer = KMeansScorer(k=5, window=int(window_size), component_wise=False) + Kmeans_scorer.fit(series_train) + anomaly_score = Kmeans_scorer.score(series) + + # detects where anomalies lie, then return binary prediction + threshold = 0.95 + detector = QuantileDetector(high_quantile=threshold) + anomaly_pred = detector.fit_detect(series=anomaly_score) + + # TODO decide on interface to backend and return useful values in useful format + anomaly_timestamps = [] + vals = anomaly_pred.all_values() + # gets timestamps of anomalies + for i in range(len(vals)): + if vals[i] == 1.0: + anomaly_timestamps.append(series.get_timestamp_at_point(i)) - def get_task_ids(self): + return anomaly_timestamps + + def task_preprocessing(backup_type, task_id, variable): + global df + # --------------- Task Specific Preprocessing ---------------# + # TODO remove when other types are implemented + if backup_type != "F": + raise ValueError( + "k means analysis for this backup type is not yet implemented" + ) + + # removes backups of types that are not specified + df = df[df.fdi_type == backup_type] + # removes backups that do not have chosen task id + df = df[df.task_uuid == task_id] + # removes columns that are not relevant (only univariate analysis supported) + df = df[["sbc_start", variable]] + + def get_task_ids(): + global df # gets all possible values for task_uuid - task_ids = self.df["task_uuid"].unique() - return jsonify(task_ids.to_dict()) + task_ids = df["task_uuid"].unique() + return dict(enumerate(task_ids.flatten(), 1)) + + def get_frequencies(task_id, backup_type, variable): + global df + + Time_series_analyzer.task_preprocessing(backup_type, task_id, variable) + freqs = df[["sbc_start"]] - def get_frequencies(self): # gets backup frequencies present in dataset - freqs = self.df.diff()["sbc_start"] + freqs = freqs.diff()["sbc_start"] # orders frequencies by count and removes NaN edge cases freqs = freqs.value_counts() freqs = freqs[freqs.notnull()] - return jsonify(freqs.to_dict()) + freqs = freqs.reset_index() # resetting index back into dataframe + + # converts into returnable format + freqs["sbc_start"] = freqs["sbc_start"].dt.total_seconds() + + return freqs.to_dict() From 238b81f1475ed3f61979b1eac9671f8ebe6f6d6e Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Mon, 9 Dec 2024 23:49:44 +0100 Subject: [PATCH 10/20] fixed repeat execution issues Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 4 +- .../metadata_analyzer/time_series_analyzer.py | 38 +++++++++++-------- apps/analyzer/poetry.lock | 2 +- apps/analyzer/pyproject.toml | 1 + 4 files changed, 27 insertions(+), 18 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 65d14db..15147ad 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -192,8 +192,8 @@ def runTimeSeriesTests(): variable, task_id, frequency, backup_type, window_size ) return jsonify(result) - except Exception as ex: - return "Misc error occurred!", 500 + except ValueError as val: + return "Value error occured: " + val.message, 401 @app.route("/getTaskIds", methods=["GET"]) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 7163b53..e8a3e3d 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -35,23 +35,27 @@ def __init__(self): df = df.sort_values("sbc_start") def k_means_analyze(variable, task_id, frequency, backup_type, window_size): - global df - - Time_series_analyzer.task_preprocessing(backup_type, task_id, variable) - - df.index = df["sbc_start"] # sets index to datetime in sbc_start column - df = df.drop( + working_df = Time_series_analyzer.task_preprocessing( + backup_type, task_id, variable + ) + + working_df.index = working_df[ + "sbc_start" + ] # sets index to datetime in sbc_start column + working_df = working_df.drop( "sbc_start", axis=1 ) # removes column because values are now in index # interpolates series to emulate backups at regular intervals, different methods possible frequency = frequency + "s" # adds unit - df = df.asfreq(frequency, method="ffill") + working_df = working_df.asfreq(frequency, method="ffill") # determines number of clusters for the k means scorer, # TODO use clusters for more useful k value clusters = len(df[variable].unique()) # initializes time series - series = TimeSeries.from_series(df, fill_missing_dates=False, freq=frequency) + series = TimeSeries.from_series( + working_df, fill_missing_dates=False, freq=frequency + ) # TODO interim definition of training data, change to something more useful series_train = series[: round(len(series) / 4)] @@ -78,6 +82,7 @@ def k_means_analyze(variable, task_id, frequency, backup_type, window_size): def task_preprocessing(backup_type, task_id, variable): global df + # --------------- Task Specific Preprocessing ---------------# # TODO remove when other types are implemented if backup_type != "F": @@ -85,12 +90,15 @@ def task_preprocessing(backup_type, task_id, variable): "k means analysis for this backup type is not yet implemented" ) + working_df = df # removes backups of types that are not specified - df = df[df.fdi_type == backup_type] + working_df = working_df[working_df.fdi_type == backup_type] # removes backups that do not have chosen task id - df = df[df.task_uuid == task_id] + working_df = working_df[working_df.task_uuid == task_id] # removes columns that are not relevant (only univariate analysis supported) - df = df[["sbc_start", variable]] + working_df = working_df[["sbc_start", variable]] + + return working_df def get_task_ids(): global df @@ -99,10 +107,10 @@ def get_task_ids(): return dict(enumerate(task_ids.flatten(), 1)) def get_frequencies(task_id, backup_type, variable): - global df - - Time_series_analyzer.task_preprocessing(backup_type, task_id, variable) - freqs = df[["sbc_start"]] + working_df = Time_series_analyzer.task_preprocessing( + backup_type, task_id, variable + ) + freqs = working_df[["sbc_start"]] # gets backup frequencies present in dataset freqs = freqs.diff()["sbc_start"] diff --git a/apps/analyzer/poetry.lock b/apps/analyzer/poetry.lock index 48f9179..5d04877 100644 --- a/apps/analyzer/poetry.lock +++ b/apps/analyzer/poetry.lock @@ -3770,4 +3770,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "04224cda7dc81a335ba6d326339dbe4b5f14158ef77d1a6a15a804bbc7d8997b" +content-hash = "bf9c648cd8adf50b3322e01968e50470baf0b58f74840bd53db96ea0f1063545" diff --git a/apps/analyzer/pyproject.toml b/apps/analyzer/pyproject.toml index 8db3da7..c40401d 100644 --- a/apps/analyzer/pyproject.toml +++ b/apps/analyzer/pyproject.toml @@ -29,6 +29,7 @@ readme = 'README.md' requests = "^2.32.3" flasgger = "^0.9.7.1" darts = "^0.31.0" + pandas = "^2.2.3" [tool.poetry.group.dev.dependencies] autopep8 = "2.0.2" From 4f8a330459fec288c9f820daa08912a462341f4f Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Mon, 9 Dec 2024 23:55:22 +0100 Subject: [PATCH 11/20] now catching exception for empty time series Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 2 +- apps/analyzer/metadata_analyzer/time_series_analyzer.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 15147ad..0361044 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -193,7 +193,7 @@ def runTimeSeriesTests(): ) return jsonify(result) except ValueError as val: - return "Value error occured: " + val.message, 401 + return "Value error occured: " + str(val), 401 @app.route("/getTaskIds", methods=["GET"]) diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index e8a3e3d..420e05a 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -57,6 +57,9 @@ def k_means_analyze(variable, task_id, frequency, backup_type, window_size): working_df, fill_missing_dates=False, freq=frequency ) + if len(series) == 0: + raise ValueError("Series had length 0 after applying specified parameters!") + # TODO interim definition of training data, change to something more useful series_train = series[: round(len(series) / 4)] From 60a35220dea9413e25086739ffbbdf7215ce3ed8 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 10:19:01 +0100 Subject: [PATCH 12/20] swagger for kMeansAnalysis Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 59 +++++++++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 0361044..b1c451b 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -171,6 +171,65 @@ def simple_rule_based_analysis_inc(): # TODO yaml for swagger @app.route("/kMeansAnomalies", methods=["POST"]) def runTimeSeriesTests(): + """Runs k-means anomaly detection on the specified dataset. + --- + parameters: + - name: input + in: body + type: object + required: true + properties: + properties: + variable: + type: string + example: 'data_size' + task_id: + type: string + example: '67de754c-b953-4098-83cd-6d34ca2960c3' + backup_type: + type: string + example: 'F' + frequency: + type: int + example: 86401 + windows_size: + type: int + example: 2 + definitions: + MeansBody: + type: object + properties: + variable: + type: string + example: 'data_size' + task_id: + type: string + example: '67de754c-b953-4098-83cd-6d34ca2960c3' + backup_type: + type: string + example: 'F' + frequency: + type: int + example: 86401 + windows_size: + type: int + example: 2 + Timestamps: + type: array + items: + type: string + example: + - 'Tue, 10 Sep 2024 21:01:22 GMT' + - 'Sat, 21 Sep 2024 21:01:33 GMT' + - 'Sun, 22 Sep 2024 21:01:34 GMT' + - 'Tue, 08 Oct 2024 21:01:50 GMT' + - 'Wed, 09 Oct 2024 21:01:51 GMT' + responses: + 200: + description: The timestamps of the anomalies + schema: + $ref: '#/definitions/Timestamps' + """ json = request.get_json() field = "None" try: From 64028add208db40a98c279fe9ee6fa34870e8f6f Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 10:49:22 +0100 Subject: [PATCH 13/20] small bug fix and other swagger defs Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 77 ++++++++++++++++++- .../metadata_analyzer/time_series_analyzer.py | 2 +- 2 files changed, 75 insertions(+), 4 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index b1c451b..b0ba1fe 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -179,8 +179,7 @@ def runTimeSeriesTests(): type: object required: true properties: - properties: - variable: + variable: type: string example: 'data_size' task_id: @@ -192,7 +191,7 @@ def runTimeSeriesTests(): frequency: type: int example: 86401 - windows_size: + window_size: type: int example: 2 definitions: @@ -257,11 +256,83 @@ def runTimeSeriesTests(): @app.route("/getTaskIds", methods=["GET"]) def return_task_ids(): + """Gets task ids of current dataset, necessary for time series analysis. + --- + definitions: + task_ids: + type: object + properties: + 1: + type: string + example: 'd6f0d862-ef51-4f01-8d34-5503a58c6421' + 2: + type: string + example: '67de754c-b953-4098-83cd-6d34ca2960c3' + 3: + type: string + example: '8cc9efbc-d392-430d-8844-af04da35e7d6' + responses: + 200: + description: All possible task ids + schema: + $ref: '#/definitions/task_ids' + """ return jsonify(Time_series_analyzer.get_task_ids()) @app.route("/getFrequenciesForTask", methods=["POST"]) def return_frequencies(): + """Gets frequencies for a specific task, variable and backup type. + --- + parameters: + - name: input + in: body + type: object + required: true + properties: + variable: + type: string + example: 'data_size' + task_id: + type: string + example: '67de754c-b953-4098-83cd-6d34ca2960c3' + backup_type: + type: string + example: 'F' + definitions: + Frequencies: + type: object + properties: + count: + type: object + properties: + 0: + type: int + example: 20 + 1: + type: int + example: 17 + 2: + type: int + example: 5 + sbc_start: + type: object + properties: + 0: + type: int + example: 86400 + 1: + type: int + example: 86401 + 2: + type: int + example: 86399 + responses: + 200: + description: All backup frequencies found that meet the conditions, ranked by times appeared + schema: + $ref: '#/definitions/Frequencies' + """ json = request.get_json() field = "None" try: diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 420e05a..6740f68 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -47,7 +47,7 @@ def k_means_analyze(variable, task_id, frequency, backup_type, window_size): ) # removes column because values are now in index # interpolates series to emulate backups at regular intervals, different methods possible - frequency = frequency + "s" # adds unit + frequency = str(frequency) + "s" # adds unit working_df = working_df.asfreq(frequency, method="ffill") # determines number of clusters for the k means scorer, # TODO use clusters for more useful k value From 2eaf052910fabf5a7255c3271e19f27461901cf8 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 11:21:48 +0100 Subject: [PATCH 14/20] minor restructuring to fit other analyzers Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/analyzer.py | 25 +++++++++++++++---- apps/analyzer/metadata_analyzer/main.py | 12 ++++++--- .../metadata_analyzer/time_series_analyzer.py | 6 ++--- 3 files changed, 32 insertions(+), 11 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/analyzer.py b/apps/analyzer/metadata_analyzer/analyzer.py index 9f8686b..52aa979 100644 --- a/apps/analyzer/metadata_analyzer/analyzer.py +++ b/apps/analyzer/metadata_analyzer/analyzer.py @@ -1,9 +1,16 @@ class Analyzer: - def init(database, backend, simple_analyzer, simple_rule_based_analyzer): + def init( + database, + backend, + simple_analyzer, + simple_rule_based_analyzer, + time_series_analyzer, + ): Analyzer.database = database Analyzer.backend = backend Analyzer.simple_analyzer = simple_analyzer Analyzer.simple_rule_based_analyzer = simple_rule_based_analyzer + Analyzer.time_series_analyzer = time_series_analyzer def analyze(): data = list(Analyzer.database.get_results()) @@ -58,13 +65,21 @@ def simple_rule_based_analysis(alert_limit): data = list(Analyzer.database.get_results()) result = Analyzer.simple_rule_based_analyzer.analyze(data, alert_limit) return result - + def simple_rule_based_analysis_diff(alert_limit): data = list(Analyzer.database.get_results()) - result = Analyzer.simple_rule_based_analyzer.analyze_diff(data,alert_limit) + result = Analyzer.simple_rule_based_analyzer.analyze_diff(data, alert_limit) return result - + def simple_rule_based_analysis_inc(alert_limit): data = list(Analyzer.database.get_results()) - result = Analyzer.simple_rule_based_analyzer.analyze_inc(data,alert_limit) + result = Analyzer.simple_rule_based_analyzer.analyze_inc(data, alert_limit) + return result + + def simple_time_series_analysis( + variable, task_id, frequency, backup_type, window_size + ): + result = Analyzer.time_series_analyzer.k_means_analyze( + variable, task_id, frequency, backup_type, window_size + ) return result diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index b0ba1fe..291acb2 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -246,7 +246,7 @@ def runTimeSeriesTests(): return "Missing field of type " + field try: - result = Time_series_analyzer.k_means_analyze( + result = Analyzer.simple_time_series_analysis( variable, task_id, frequency, backup_type, window_size ) return jsonify(result) @@ -352,9 +352,15 @@ def main(): database = Database() backend = Backend(os.getenv("BACKEND_URL")) simple_analyzer = SimpleAnalyzer() - time_series_analyzer = Time_series_analyzer() + time_series_analyzer = Time_series_analyzer(database) simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, simple_analyzer, simple_rule_based_analyzer) + Analyzer.init( + database, + backend, + simple_analyzer, + simple_rule_based_analyzer, + time_series_analyzer, + ) print(f"FLASK_RUN_HOST: {os.getenv('FLASK_RUN_HOST')}") print(f"FLASK_RUN_PORT: {os.getenv('FLASK_RUN_PORT')}") diff --git a/apps/analyzer/metadata_analyzer/time_series_analyzer.py b/apps/analyzer/metadata_analyzer/time_series_analyzer.py index 6740f68..a364cbe 100644 --- a/apps/analyzer/metadata_analyzer/time_series_analyzer.py +++ b/apps/analyzer/metadata_analyzer/time_series_analyzer.py @@ -11,10 +11,10 @@ class Time_series_analyzer: df = "" - def __init__(self): + def __init__(self, database): global df # Create an engine using shared init - database = Database() + database = database # read table into a dataframe with database.engine.connect() as conn, conn.begin(): df = pd.read_sql_table("results", conn) @@ -34,7 +34,7 @@ def __init__(self): # sorts dataframe by sbc_start df = df.sort_values("sbc_start") - def k_means_analyze(variable, task_id, frequency, backup_type, window_size): + def k_means_analyze(self, variable, task_id, frequency, backup_type, window_size): working_df = Time_series_analyzer.task_preprocessing( backup_type, task_id, variable ) From 4126143c7869d95698ec5080823121bd3240a872 Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 11:30:13 +0100 Subject: [PATCH 15/20] fixed tests Signed-off-by: Amelie Regl --- apps/analyzer/tests/test_main.py | 5 +- .../tests/test_simple_rule_based_analyzer.py | 395 ++++++++++++------ 2 files changed, 270 insertions(+), 130 deletions(-) diff --git a/apps/analyzer/tests/test_main.py b/apps/analyzer/tests/test_main.py index c0a3b8b..9634f4f 100644 --- a/apps/analyzer/tests/test_main.py +++ b/apps/analyzer/tests/test_main.py @@ -8,10 +8,12 @@ from tests.mock_backend import MockBackend from tests.mock_database import MockDatabase + def test_hello_world(): """Test the hello_world function.""" assert hello_world() == "Hello, world!" + def test_update_data(): mock_result = Result() mock_result.uuid = "1" @@ -21,8 +23,7 @@ def test_update_data(): database = MockDatabase([mock_result]) backend = MockBackend() - Analyzer.init(database, backend, None, None) + Analyzer.init(database, backend, None, None, None) Analyzer.update_data() assert backend.backups == [Analyzer._convert_result(mock_result)] - diff --git a/apps/analyzer/tests/test_simple_rule_based_analyzer.py b/apps/analyzer/tests/test_simple_rule_based_analyzer.py index e2b7d7b..f823a9c 100644 --- a/apps/analyzer/tests/test_simple_rule_based_analyzer.py +++ b/apps/analyzer/tests/test_simple_rule_based_analyzer.py @@ -18,110 +18,151 @@ def _create_mock_result(task, uuid, fdi_type, data_size, start_time): def test_alert(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] def test_alerts_different_tasks(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("bar", "1", "F", 200_000_000, datetime.fromisoformat("2000-01-01")) - mock_result4 = _create_mock_result("bar", "2", "F", 100_000_000, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "bar", "1", "F", 200_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result4 = _create_mock_result( + "bar", "2", "F", 100_000_000, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }, { - "size": mock_result4.data_size / 1_000_000, - "referenceSize": mock_result3.data_size / 1_000_000, - "backupId": mock_result4.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + }, + { + "size": mock_result4.data_size / 1_000_000, + "referenceSize": mock_result3.data_size / 1_000_000, + "backupId": mock_result4.uuid, + }, + ] def test_alert_backup_size_zero(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 0, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 0, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] def test_no_alert_size_diff_too_small(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 120_000_000, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 120_000_000, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) assert backend.alerts == [] def test_no_alert_wrong_type(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 121_000_000, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 121_000_000, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) assert backend.alerts == [] def test_no_alert_different_tasks(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("bar", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "bar", "2", "F", 121_000_000, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) assert backend.alerts == [] def test_alert_limit(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 150_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "F", 200_000_000, datetime.fromisoformat("2000-01-03")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 150_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "F", 200_000_000, datetime.fromisoformat("2000-01-03") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(1) assert len(backend.alerts) == 1 @@ -129,32 +170,44 @@ def test_alert_limit(): # extremely large difference def test_alert_backup_size_zero_diff(): - mock_result1 = _create_mock_result("foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 0, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 0, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] # two decreasing diff backups (in the accepted range) with different full backups as base def test_alert_backup_size_decrease_ok_diff(): - mock_result1 = _create_mock_result("foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "F", 100_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "D", 99_000_000, datetime.fromisoformat("2000-01-03")) + mock_result1 = _create_mock_result( + "foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "F", 100_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "D", 99_000_000, datetime.fromisoformat("2000-01-03") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) assert backend.alerts == [] @@ -162,51 +215,71 @@ def test_alert_backup_size_decrease_ok_diff(): # two decreasing diff backups (in the accepted range) with same full backup as base def test_alert_backup_size_decrease_nok_diff(): - mock_result1 = _create_mock_result("foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 99_000_000, datetime.fromisoformat("2000-01-03")) + mock_result1 = _create_mock_result( + "foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 99_000_000, datetime.fromisoformat("2000-01-03") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] # two decreasing diff backups (not in the accepted range) with same full backup as base def test_alert_backup_size_decrease_large_nok_diff(): - mock_result1 = _create_mock_result("foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-03")) + mock_result1 = _create_mock_result( + "foo", "1", "D", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-03") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] # two decreasing diff backups (not in the accepted range) with different full backups as base def test_alert_backup_size_decrease_large_ok_diff(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03")) - mock_result4 = _create_mock_result("foo", "4", "D", 1_000_000, datetime.fromisoformat("2000-01-04")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03") + ) + mock_result4 = _create_mock_result( + "foo", "4", "D", 1_000_000, datetime.fromisoformat("2000-01-04") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) assert backend.alerts == [] @@ -214,34 +287,50 @@ def test_alert_backup_size_decrease_large_ok_diff(): # two increasing diff backups (not in the accepted range) with same full backups as base def test_alert_backup_size_increase_large_nok_diff(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "D", 100_000_000, datetime.fromisoformat("2000-01-03")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "D", 100_000_000, datetime.fromisoformat("2000-01-03") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) - assert backend.alerts == [{ - "size": mock_result3.data_size / 1_000_000, - "referenceSize": mock_result2.data_size / 1_000_000, - "backupId": mock_result3.uuid - }] + assert backend.alerts == [ + { + "size": mock_result3.data_size / 1_000_000, + "referenceSize": mock_result2.data_size / 1_000_000, + "backupId": mock_result3.uuid, + } + ] # two increasing diff backups (not in the accepted range) with different full backups as base def test_alert_backup_size_increase_large_ok_diff(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03")) - mock_result4 = _create_mock_result("foo", "4", "D", 1_000_000, datetime.fromisoformat("2000-01-04")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03") + ) + mock_result4 = _create_mock_result( + "foo", "4", "D", 1_000_000, datetime.fromisoformat("2000-01-04") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) assert backend.alerts == [] @@ -250,57 +339,96 @@ def test_alert_backup_size_increase_large_ok_diff(): def test_alert_backup_size_complex_nok_diff(): - mock_result1 = _create_mock_result("foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02")) - mock_result3 = _create_mock_result("foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03")) - mock_result4 = _create_mock_result("foo", "4", "F", 1_000_000, datetime.fromisoformat("2000-01-04")) - mock_result5 = _create_mock_result("foo", "5", "D", 100_000_000, datetime.fromisoformat("2000-01-05")) - mock_result6 = _create_mock_result("foo", "6", "D", 101_000_000, datetime.fromisoformat("2000-01-06")) - mock_result7 = _create_mock_result("foo", "7", "D", 1_000_000, datetime.fromisoformat("2000-01-07")) + mock_result1 = _create_mock_result( + "foo", "1", "F", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "D", 1_000_000, datetime.fromisoformat("2000-01-02") + ) + mock_result3 = _create_mock_result( + "foo", "3", "F", 100_000_000, datetime.fromisoformat("2000-01-03") + ) + mock_result4 = _create_mock_result( + "foo", "4", "F", 1_000_000, datetime.fromisoformat("2000-01-04") + ) + mock_result5 = _create_mock_result( + "foo", "5", "D", 100_000_000, datetime.fromisoformat("2000-01-05") + ) + mock_result6 = _create_mock_result( + "foo", "6", "D", 101_000_000, datetime.fromisoformat("2000-01-06") + ) + mock_result7 = _create_mock_result( + "foo", "7", "D", 1_000_000, datetime.fromisoformat("2000-01-07") + ) database = MockDatabase( - [mock_result1, mock_result2, mock_result3, mock_result4, mock_result5, mock_result6, mock_result7]) + [ + mock_result1, + mock_result2, + mock_result3, + mock_result4, + mock_result5, + mock_result6, + mock_result7, + ] + ) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(1) - assert backend.alerts == [{ - "size": mock_result7.data_size / 1_000_000, - "referenceSize": mock_result6.data_size / 1_000_000, - "backupId": mock_result7.uuid - }] + assert backend.alerts == [ + { + "size": mock_result7.data_size / 1_000_000, + "referenceSize": mock_result6.data_size / 1_000_000, + "backupId": mock_result7.uuid, + } + ] # large increase of inc size def test_alert_backup_size_zero_inc(): - mock_result1 = _create_mock_result("foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "I", 0, datetime.fromisoformat("2000-01-02")) + mock_result1 = _create_mock_result( + "foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "I", 0, datetime.fromisoformat("2000-01-02") + ) database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_inc(1) - assert backend.alerts == [{ - "size": mock_result2.data_size / 1_000_000, - "referenceSize": mock_result1.data_size / 1_000_000, - "backupId": mock_result2.uuid - }] + assert backend.alerts == [ + { + "size": mock_result2.data_size / 1_000_000, + "referenceSize": mock_result1.data_size / 1_000_000, + "backupId": mock_result2.uuid, + } + ] # irregular backup times that should not be alerted def test_alert_backup_size_irregular_inc(): - mock_result1 = _create_mock_result("foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-01")) - mock_result2 = _create_mock_result("foo", "2", "I", 0, datetime.fromisoformat("2000-01-08")) - mock_result3 = _create_mock_result("foo", "3", "I", 100_000_000, datetime.fromisoformat("2000-01-09")) - mock_result4 = _create_mock_result("foo", "4", "I", 100_000_000, datetime.fromisoformat("2000-01-10")) + mock_result1 = _create_mock_result( + "foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-01") + ) + mock_result2 = _create_mock_result( + "foo", "2", "I", 0, datetime.fromisoformat("2000-01-08") + ) + mock_result3 = _create_mock_result( + "foo", "3", "I", 100_000_000, datetime.fromisoformat("2000-01-09") + ) + mock_result4 = _create_mock_result( + "foo", "4", "I", 100_000_000, datetime.fromisoformat("2000-01-10") + ) database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_inc(1) assert backend.alerts == [] @@ -308,20 +436,31 @@ def test_alert_backup_size_irregular_inc(): # irregular backup sizes def test_alert_backup_size_irregularSize_inc(): - mock_result1 = _create_mock_result("foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-07")) - mock_result2 = _create_mock_result("foo", "2", "I", 100_000_000, datetime.fromisoformat("2000-01-08")) - mock_result3 = _create_mock_result("foo", "3", "I", 72_000_000, datetime.fromisoformat("2000-01-09")) - mock_result4 = _create_mock_result("foo", "4", "I", 100_000_000, datetime.fromisoformat("2000-01-10")) - avg = (mock_result1.data_size + mock_result2.data_size + mock_result3.data_size + mock_result4.data_size) / 4 + mock_result1 = _create_mock_result( + "foo", "1", "I", 100_000_000, datetime.fromisoformat("2000-01-07") + ) + mock_result2 = _create_mock_result( + "foo", "2", "I", 100_000_000, datetime.fromisoformat("2000-01-08") + ) + mock_result3 = _create_mock_result( + "foo", "3", "I", 72_000_000, datetime.fromisoformat("2000-01-09") + ) + mock_result4 = _create_mock_result( + "foo", "4", "I", 100_000_000, datetime.fromisoformat("2000-01-10") + ) + avg = ( + mock_result1.data_size + + mock_result2.data_size + + mock_result3.data_size + + mock_result4.data_size + ) / 4 database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_inc(1) - assert backend.alerts == [{ - "size": 72, - "referenceSize": avg / 1_000_000, - "backupId": mock_result3.uuid - }] + assert backend.alerts == [ + {"size": 72, "referenceSize": avg / 1_000_000, "backupId": mock_result3.uuid} + ] From 8c39ae84f4765eac8c531f200be90146944cbe9f Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 14:01:06 +0100 Subject: [PATCH 16/20] adjusted and fixed existing tests Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/analyzer.py | 4 +-- apps/analyzer/tests/test_analyzer.py | 4 +-- .../tests/test_simple_rule_based_analyzer.py | 36 +++++++++---------- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/analyzer.py b/apps/analyzer/metadata_analyzer/analyzer.py index 27eb060..9b043d3 100644 --- a/apps/analyzer/metadata_analyzer/analyzer.py +++ b/apps/analyzer/metadata_analyzer/analyzer.py @@ -68,7 +68,7 @@ def _send_Backups(): for result in results: # Only send real backups - if result.is_backup <= 0: + if (result.is_backup is not None) and (result.is_backup <= 0): continue # Only send backups where the relevant data is not null @@ -141,7 +141,7 @@ def simple_time_series_analysis( variable, task_id, frequency, backup_type, window_size ): result = Analyzer.time_series_analyzer.k_means_analyze( - variable, task_id, frequency, backup_type, window_size + variable, task_id, frequency, backup_type, window_size) def simple_rule_based_analysis_creation_dates(alert_limit): data = list(Analyzer.database.get_results()) diff --git a/apps/analyzer/tests/test_analyzer.py b/apps/analyzer/tests/test_analyzer.py index 2ce19ca..82f8e25 100644 --- a/apps/analyzer/tests/test_analyzer.py +++ b/apps/analyzer/tests/test_analyzer.py @@ -38,7 +38,7 @@ def test_update_data_all_types(): database = MockDatabase(mock_results, mock_tasks) backend = MockBackend() - Analyzer.init(database, backend, None, None) + Analyzer.init(database, backend, None, None, None) Analyzer.update_data() assert backend.backups == [{ @@ -84,7 +84,7 @@ def test_update_data_not_a_backup(): database = MockDatabase([mock_result1], []) backend = MockBackend() - Analyzer.init(database, backend, None, None) + Analyzer.init(database, backend, None, None, None) Analyzer.update_data() assert backend.backups == [] diff --git a/apps/analyzer/tests/test_simple_rule_based_analyzer.py b/apps/analyzer/tests/test_simple_rule_based_analyzer.py index 196089c..75f33a7 100644 --- a/apps/analyzer/tests/test_simple_rule_based_analyzer.py +++ b/apps/analyzer/tests/test_simple_rule_based_analyzer.py @@ -181,7 +181,7 @@ def test_alert_full_start_date(): backend = MockBackend() backend.set_latest_alert_id("SIZE_ALERT", "FULL", "2") simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis(-1) assert backend.size_alerts == [{ @@ -222,7 +222,7 @@ def test_alert_diff_start_date(): backend = MockBackend() backend.set_latest_alert_id("SIZE_ALERT", "DIFFERENTIAL", "2") simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_diff(-1) assert backend.size_alerts == [{ @@ -452,7 +452,7 @@ def test_alert_diff_start_date(): backend = MockBackend() backend.set_latest_alert_id("SIZE_ALERT", "INCREMENTAL", "2") simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_inc(-1) assert backend.size_alerts == [{ @@ -530,7 +530,7 @@ def test_alert_exact_time(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [] @@ -546,7 +546,7 @@ def test_alert_small_time_deviation(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4, mock_result5]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [] @@ -559,7 +559,7 @@ def test_alert_time_on_the_limit(): database = MockDatabase([mock_result1, mock_result2]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [] @@ -575,7 +575,7 @@ def test_alert_moving_schedule(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4, mock_result5]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [] @@ -591,7 +591,7 @@ def test_alert_time_midnight_no_alert(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4, mock_result5]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [] @@ -606,7 +606,7 @@ def test_alert_unusual_time(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -625,7 +625,7 @@ def test_alert_two_different_schedules_same_task(): database = MockDatabase([mock_result1, mock_result2, mock_result3, mock_result4]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -643,7 +643,7 @@ def test_alert_time_midnight_alerts(): database = MockDatabase([mock_result1, mock_result2, mock_result3]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -666,7 +666,7 @@ def test_alert_creation_date_unordered(): database = MockDatabase([mock_result4, mock_result2, mock_result3, mock_result1]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -689,7 +689,7 @@ def test_alert_creation_date_different_tasks(): database = MockDatabase([mock_result4, mock_result2, mock_result3, mock_result1]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -709,7 +709,7 @@ def test_alert_latest_creation_date(): backend = MockBackend() backend.set_latest_alert_id("CREATION_DATE_ALERT", None, "3") simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_creation_dates(-1) assert backend.creation_date_alerts == [{ @@ -728,7 +728,7 @@ def test_storage_fill_alert_empty(): database = MockDatabase([], [], [mock_data_store1]) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_storage_capacity(-1) assert backend.storage_fill_alerts == [] @@ -745,7 +745,7 @@ def test_storage_fill_alert_enough_capacity_left(): ) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_storage_capacity(-1) assert backend.storage_fill_alerts == [] @@ -762,7 +762,7 @@ def test_storage_fill_alert_enough_capacity_left(): ) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_storage_capacity(-1) assert backend.storage_fill_alerts == [ @@ -798,7 +798,7 @@ def test_storage_fill_alert_missing_data(): ) backend = MockBackend() simple_rule_based_analyzer = SimpleRuleBasedAnalyzer(backend, 0.2, 0.2, 0.2, 0.2) - Analyzer.init(database, backend, None, simple_rule_based_analyzer) + Analyzer.init(database, backend, None, simple_rule_based_analyzer, None) Analyzer.simple_rule_based_analysis_storage_capacity(-1) assert backend.storage_fill_alerts == [] From 3a93d0c88e7a70b984a45227097039f3dad9b96f Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 16:36:42 +0100 Subject: [PATCH 17/20] try to fix poetry lock Signed-off-by: Amelie Regl --- apps/analyzer/poetry.lock | 68 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 66 insertions(+), 2 deletions(-) diff --git a/apps/analyzer/poetry.lock b/apps/analyzer/poetry.lock index bd32826..5276721 100644 --- a/apps/analyzer/poetry.lock +++ b/apps/analyzer/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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python-versions = ">=3.9,<3.12" -content-hash = "d13a176520244868333df1baafcc22bd23a0c1a2da8b84f2e3d31fac686076a9" +content-hash = "c68c6849198e6e1309e589331a4df4d644d8f32524cd0a233e3523b6a4809275" diff --git a/apps/analyzer/pyproject.toml b/apps/analyzer/pyproject.toml index 3f11a4a..9b17bbe 100644 --- a/apps/analyzer/pyproject.toml +++ b/apps/analyzer/pyproject.toml @@ -30,6 +30,7 @@ readme = 'README.md' flasgger = "^0.9.7.1" darts = "^0.31.0" pandas = "^2.2.3" + mypy = "^1.13.0" [tool.poetry.group.dev.dependencies] autopep8 = "2.0.2" From d8f19372f9540ee48b9ad0b38514e8a7e00fb2df Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 17:04:10 +0100 Subject: [PATCH 19/20] changed response codes to more fitting values Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 8ed82bc..01d1c21 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -433,7 +433,7 @@ def runTimeSeriesTests(): ) return jsonify(result) except ValueError as val: - return "Value error occured: " + str(val), 401 + return "Value error occured: " + str(val), 400 @app.route("/getTaskIds", methods=["GET"]) @@ -525,7 +525,7 @@ def return_frequencies(): field = "variable" variable = json["variable"] except KeyError: - return "Missing field of type " + field + return "Missing field of type " + field, 400 return jsonify(Time_series_analyzer.get_frequencies(task_id, backup_type, variable)) From 7a98621fe9d9e22178c705c1d577fcba44f8a09a Mon Sep 17 00:00:00 2001 From: Amelie Regl Date: Tue, 10 Dec 2024 17:10:19 +0100 Subject: [PATCH 20/20] number change Signed-off-by: Amelie Regl --- apps/analyzer/metadata_analyzer/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/apps/analyzer/metadata_analyzer/main.py b/apps/analyzer/metadata_analyzer/main.py index 01d1c21..374a13c 100644 --- a/apps/analyzer/metadata_analyzer/main.py +++ b/apps/analyzer/metadata_analyzer/main.py @@ -425,7 +425,7 @@ def runTimeSeriesTests(): field = "window_size" window_size = json["window_size"] except KeyError: - return "Missing field of type " + field + return "Missing field of type " + field, 400 try: result = Analyzer.simple_time_series_analysis(