From d0dca7fb4e8195ab2498a3de6e639fe63861c8e0 Mon Sep 17 00:00:00 2001 From: Caracol Date: Tue, 15 Oct 2024 13:00:44 -0300 Subject: [PATCH 01/10] added find_df_transitions to _bibmon_tools --- .vscode/settings.json | 3 +++ bibmon/_bibmon_tools.py | 49 ++++++++++++++++++++++++++++++++++++++++- 2 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 .vscode/settings.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7c2feb7 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "editor.formatOnSave": false +} diff --git a/bibmon/_bibmon_tools.py b/bibmon/_bibmon_tools.py index fd598d9..e2f36fb 100644 --- a/bibmon/_bibmon_tools.py +++ b/bibmon/_bibmon_tools.py @@ -2,6 +2,7 @@ import pandas as pd from datetime import datetime import matplotlib.pyplot as plt +from typing import Literal ############################################################################### @@ -692,4 +693,50 @@ def comparative_table (models, X_train, X_validation, X_test, return_tables.append(times_df) - return return_tables \ No newline at end of file + return return_tables + +############################################################################## + +def find_df_transitions( + df: pd.DataFrame, + threshold: float = 1, + data_type: Literal["string", "number"] = "number", + label: str = None, +) -> list[int]: + """ + Finds transitions in a DataFrame. This can be used to find indices of interesting events in the data. + + Parameters + ---------- + df: pandas.DataFrame + Data to be analyzed. + threshold: float, optional + Threshold to be used in the transition detection, this is the minimum difference between two consecutive points. Will be used only if data_type is 'number'. + data_type: str, optional + Type of data to be analyzed. If 'number', the threshold will be used to detect transitions. If 'string', the function will look for changes in the values. + label: str + Label to be used in the transition detection. + + Returns + ---------- + : list of ints + Indices of the transitions. + """ + + if label is None: + return [] + + transitions = [] + previous_event = df[label].iloc[0] + + for i in range(1, len(df)): + if data_type == "number": + if abs(df[label].iloc[i] - previous_event) > threshold: + transitions.append(i) + previous_event = df[label].iloc[i] + elif data_type == "string": + if df[label].iloc[i] != previous_event: + transitions.append(i) + previous_event = df[label].iloc[i] + + return transitions \ No newline at end of file From 3b72b577adcf00d28be7771926bd4133817a8aab Mon Sep 17 00:00:00 2001 From: Caracol Date: Tue, 15 Oct 2024 13:22:38 -0300 Subject: [PATCH 02/10] Created test requirements and implemented find_df_transitions unity test --- CONTRIBUTING.md | 16 +++++++++++++++- test/requirements.txt | 1 + test/test_tools.py | 10 +++++++++- 3 files changed, 25 insertions(+), 2 deletions(-) create mode 100644 test/requirements.txt diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e0a93e5..eac021d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -109,4 +109,18 @@ To apply the new logic in the library, it will be necessary to implement the use ### Additional Features -Preferably, use the `_bibmon_tools.py` file to implement additional features. \ No newline at end of file +Preferably, use the `_bibmon_tools.py` file to implement additional features. + +### Testing New Functionalities + +The first step to add new functionalities is to download the testing libraries. To do this, run the following command: + +```bash +pip install -r test/requirements.txt +``` + +After implementing the new functionalities, run the tests to ensure that the new code is working correctly. To do this, run the following command: + +```bash +pytest +``` \ No newline at end of file diff --git a/test/requirements.txt b/test/requirements.txt new file mode 100644 index 0000000..c714a35 --- /dev/null +++ b/test/requirements.txt @@ -0,0 +1 @@ +pytest>=8.x \ No newline at end of file diff --git a/test/test_tools.py b/test/test_tools.py index 03f0d5d..f9e74ef 100644 --- a/test/test_tools.py +++ b/test/test_tools.py @@ -60,4 +60,12 @@ def test_complete_analysis(): fault_start = '2018-01-02 06:00:00', fault_end = '2018-01-02 09:00:00') - model.plot_importances() \ No newline at end of file + model.plot_importances() + + +def test_find_df_transitions(): + data = bibmon.load_real_data() + + transitions = bibmon._bibmon_tools.find_df_transitions(data, 1, "number", "tag101") + + assert transitions == [99, 101, 102, 103, 104, 106, 107, 108, 243] \ No newline at end of file From 72be3cc0430c5feec687c12909c3afea9ced9a51 Mon Sep 17 00:00:00 2001 From: Caracol Date: Tue, 15 Oct 2024 13:33:25 -0300 Subject: [PATCH 03/10] implemented df splitting tool and its unity test --- .gitignore | 2 ++ bibmon/_bibmon_tools.py | 38 +++++++++++++++++++++++++++++++++++++- test/test_tools.py | 19 ++++++++++++++++++- 3 files changed, 57 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 770002a..0126e61 100644 --- a/.gitignore +++ b/.gitignore @@ -172,3 +172,5 @@ dmypy.json # Pyre type checker .pyre/ +# Temporary files +temp.py \ No newline at end of file diff --git a/bibmon/_bibmon_tools.py b/bibmon/_bibmon_tools.py index e2f36fb..210b961 100644 --- a/bibmon/_bibmon_tools.py +++ b/bibmon/_bibmon_tools.py @@ -739,4 +739,40 @@ def find_df_transitions( transitions.append(i) previous_event = df[label].iloc[i] - return transitions \ No newline at end of file + return transitions + +############################################################################### + +def split_df_percentages(df: pd.DataFrame, percentages: list[float]) -> list[pd.DataFrame]: + """ + Splits a DataFrame into multiple DataFrames according to the given percentages, the sum of percentages must equal 1. + + For example, if percentage = [0.6, 0.2, 0.2], the function will return a list with three DataFrames, the first one with 60% of the data, the second one with 20% and the third one with 20%. + + Warning: This function may cause data loss if the split cannot be done exactly according to the percentages. + + Parameters + ---------- + df: pandas.DataFrame + Data to be split. + percentages: list of floats + List of percentages to be used in the split. + + Returns + ---------- + : list of pandas.DataFrames + List with the split DataFrames. + """ + + if sum(percentages) != 1: + raise ValueError("The sum of the percentages must be 1.") + + split_dfs = [] + start = 0 + + for i in range(len(percentages)): + end = start + int(percentages[i] * len(df)) + split_dfs.append(df.iloc[start:end]) + start = end + + return split_dfs \ No newline at end of file diff --git a/test/test_tools.py b/test/test_tools.py index f9e74ef..db21c1b 100644 --- a/test/test_tools.py +++ b/test/test_tools.py @@ -68,4 +68,21 @@ def test_find_df_transitions(): transitions = bibmon._bibmon_tools.find_df_transitions(data, 1, "number", "tag101") - assert transitions == [99, 101, 102, 103, 104, 106, 107, 108, 243] \ No newline at end of file + assert transitions == [99, 101, 102, 103, 104, 106, 107, 108, 243] + +def test_split_df_percentages(): + data = bibmon.load_real_data() + + splitted = bibmon._bibmon_tools.split_df_percentages(data, [0.6, 0.2, 0.2]) + + assert splitted[0].shape[0] == 1901 + assert splitted[1].shape[0] == 633 + assert splitted[2].shape[0] == 633 + +def test_split_df_percentages_error(): + data = bibmon.load_real_data() + + try: + _ = bibmon._bibmon_tools.split_df_percentages(data, [0.6, 0.2]) + except ValueError: + assert True \ No newline at end of file From 65c119d71cc1ac45ccc3ce45839f481ed6081f80 Mon Sep 17 00:00:00 2001 From: Caracol Date: Tue, 15 Oct 2024 15:01:39 -0300 Subject: [PATCH 04/10] Added 3w dataset example to code base, added 3w loader and tooling for ini inspection --- bibmon/__init__.py | 4 +- bibmon/_load_data.py | 35 +- bibmon/three_w/LICENSE-CC-BY | 395 ++++++++++++++++++ bibmon/three_w/README.md | 83 ++++ .../three_w/WELL-00019_20120601165020.parquet | Bin 0 -> 5767996 bytes bibmon/three_w/__init__.py | 1 + bibmon/three_w/dataset.ini | 140 +++++++ bibmon/three_w/tools.py | 10 + requirements.txt | 3 +- 9 files changed, 666 insertions(+), 5 deletions(-) create mode 100644 bibmon/three_w/LICENSE-CC-BY create mode 100644 bibmon/three_w/README.md create mode 100644 bibmon/three_w/WELL-00019_20120601165020.parquet create mode 100644 bibmon/three_w/__init__.py create mode 100644 bibmon/three_w/dataset.ini create mode 100644 bibmon/three_w/tools.py diff --git a/bibmon/__init__.py b/bibmon/__init__.py index 3dd3ee7..373870a 100644 --- a/bibmon/__init__.py +++ b/bibmon/__init__.py @@ -4,12 +4,12 @@ from ._sbm import SBM from ._sklearn_regressor import sklearnRegressor from ._preprocess import PreProcess -from ._load_data import load_tennessee_eastman, load_real_data +from ._load_data import load_tennessee_eastman, load_real_data, load_3w from ._bibmon_tools import train_val_test_split, complete_analysis, comparative_table, spearmanr_dendrogram, create_df_with_dates, create_df_with_noise, align_dfs_by_rows __all__ = ['Autoencoder','PCA','ESN','SBM', 'sklearnRegressor', 'PreProcess', - 'load_tennessee_eastman', 'load_real_data', + 'load_tennessee_eastman', 'load_real_data', 'load_3_w', 'train_val_test_split', 'complete_analysis', 'comparative_table', 'spearmanr_dendrogram', 'create_df_with_dates', 'create_df_with_noise', 'align_dfs_by_rows'] diff --git a/bibmon/_load_data.py b/bibmon/_load_data.py index 85d160f..fb481dd 100644 --- a/bibmon/_load_data.py +++ b/bibmon/_load_data.py @@ -1,9 +1,10 @@ import os import pandas as pd import importlib.resources as pkg_resources +from typing import Literal from ._bibmon_tools import create_df_with_dates -from . import real_process_data, tennessee_eastman +from . import real_process_data, tennessee_eastman, three_w ############################################################################### @@ -87,4 +88,34 @@ def load_real_data (): """ with pkg_resources.path(real_process_data,'real_process_data.csv') as file: - return pd.read_csv(file,index_col = 0, parse_dates = True) \ No newline at end of file + return pd.read_csv(file,index_col = 0, parse_dates = True) + +############################################################################### + +AVAILABLE_3W_CLASSES = ['8'] + +def load_3w (dataset_class: Literal['8'] = '8'): + """ + Load the '3W-8' benchmark data. + + Parameters + ---------- + dataset_class: string + Identifier of the dataset class. + Available classes: '8'. + Returns + ---------- + : pandas.DataFrame + Process data. + """ + + if dataset_class != '8': + raise ValueError(f'Dataset class not available. Available classes: {AVAILABLE_3W_CLASSES}') + + with pkg_resources.path(three_w,'WELL-00019_20120601165020.parquet') as file: + with pkg_resources.path(three_w, 'dataset.ini') as path: + ini = three_w.tools.parse_dataset_ini(path) + return pd.read_parquet( + file, + engine=ini.get("PARQUET_SETTINGS", "PARQUET_ENGINE"), + ) diff --git a/bibmon/three_w/LICENSE-CC-BY b/bibmon/three_w/LICENSE-CC-BY new file mode 100644 index 0000000..52bd145 --- /dev/null +++ b/bibmon/three_w/LICENSE-CC-BY @@ -0,0 +1,395 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. Distribution of +Creative Commons public licenses does not create a lawyer-client or +other relationship. Creative Commons makes its licenses and related +information available on an "as-is" basis. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. \ No newline at end of file diff --git a/bibmon/three_w/README.md b/bibmon/three_w/README.md new file mode 100644 index 0000000..d37506f --- /dev/null +++ b/bibmon/three_w/README.md @@ -0,0 +1,83 @@ +[![CC BY 4.0][cc-by-shield]][cc-by] +[![Versioning][semver-shield]][semver] + +[cc-by]: http://creativecommons.org/licenses/by/4.0/ +[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg +[semver]: https://semver.org +[semver-shield]: https://img.shields.io/badge/semver-2.0.0-blue + +# Table of Content + +* [Introduction](#introduction) +* [Release Notes](#release-notes) + * [1.0.0](#100) + * [1.1.0](#110) + * [1.1.1](#111) + * [2.0.0](#200) + +# Introduction + +All 3W Dataset data files (files in the subdirectories of the [dataset](dataset) directory) are licensed under the [Creative Commons Attribution 4.0 International License][cc-by]. + +# Release Notes + +Each subsection below contains release notes for a specific 3W Dataset version. Differences from the immediately previous version are highlighted. + +## 1.0.0 + +Release: July 1, 2019. + +This was the first published version, which is fully described in [this](https://doi.org/10.1016/j.petrol.2019.106223) paper. + +## 1.1.0 + +Release: December 30, 2022. + +Highlights: + +1. New instances were added as follows: + * 1 instance of event type 7. +1. Instances were removed due to issues identified as follows: + * 3 instances of event type 0; + * 1 instance of event type 5; + * 3 instances of event type 8, when compared to what is described in the paper **A realistic and public dataset with rare undesirable real events in oil wells** published in the **Journal of Petroleum Science and Engineering** (link [here](https://doi.org/10.1016/j.petrol.2019.106223)). +1. Normal periods of certain instances with anomalies were increased as possible. We tried to have instances with minimum normal periods of 1 hour; +1. Names of certain files with instances have changed due to increased normal periods; +1. Labels in some real instances were adjusted by experts; +1. All values of some variables in some real instances were corrected due to corrections in historian systems' tag configurations; +1. Certain variable values ​​have undergone minimal change due to different rounding; +1. The 3W Dataset's main configuration file ([dataset.ini](dataset.ini)) was updated. + +## 1.1.1 + +Release: April 09, 2023. + +Highlights: + +1. Issue #60 was resolved; +1. Issue #65 was resolved; +1. Certain variable values ​​have undergone minimal change due to different rounding; +1. The 3W Dataset's main configuration file ([dataset.ini](dataset.ini)) was updated. + +## 2.0.0 + +Release: July 25, 2024. + +Highlights: + +1. All instances are now saved in Parquet files (created with the `pyarrow` engine and `brotli` compression); +1. Reduction in disk space occupied by the 3W Dataset of 3.15 GB (from 4.89 GB to 1.74 GB); +1. Real and simulated instances of type 9 were added; +1. Several instances of types 0, 3, 4, 5, 6 and 8 were added; +1. Another 24 real wells were covered with new real instances (now 42 real wells are covered); +1. Some real instances, mainly of type 1, were removed; +1. 1 variable was removed (`T-JUS-CKGL`); +1. Another 20 variables were added (there are now 27 variables); +1. Another label referring to well operational status was added; +1. Normal periods in several real instances with unwanted events were extended; +1. All labeling gaps in real instances were eliminated (all observations were labeled); +1. Conversions between measurement units in several instances were corrected; +1. Labels in several real instances were adjusted by experts; +1. All values of some variables in some real instances were corrected due to corrections in historian systems' tag configurations; +1. Certain variable values ​​have undergone minimal change due to different rounding; +1. 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b/bibmon/three_w/dataset.ini @@ -0,0 +1,140 @@ +# 3W Dataset's main configuration file. +# +# All settings inherent in the 3W Dataset that can be used by your +# consumers, including the 3W Toolkit, are maintained in this file. +# In this file, we use the configuration language supported by the +# configparser module. + +# Versions in gereral +# +[VERSION] +# 3W Dataset version (may be different than 3W Toolkit version) +DATASET = 2.0.0 + +# Parquet files properties in general +# +[PARQUET_FILE_PROPERTIES] +timestamp = Instant at which observation was generated +ABER-CKGL = Opening of the GLCK (gas lift choke) [%%] +ABER-CKP = Opening of the PCK (production choke) [%%] +ESTADO-DHSV = State of the DHSV (downhole safety valve) [0, 0.5, or 1] +ESTADO-M1 = State of the PMV (production master valve) [0, 0.5, or 1] +ESTADO-M2 = State of the AMV (annulus master valve) [0, 0.5, or 1] +ESTADO-PXO = State of the PXO (pig-crossover) valve [0, 0.5, or 1] +ESTADO-SDV-GL = State of the gas lift SDV (shutdown valve) [0, 0.5, or 1] +ESTADO-SDV-P = State of the production SDV (shutdown valve) [0, 0.5, or 1] +ESTADO-W1 = State of the PWV (production wing valve) [0, 0.5, or 1] +ESTADO-W2 = State of the AWV (annulus wing valve) [0, 0.5, or 1] +ESTADO-XO = State of the XO (crossover) valve [0, 0.5, or 1] +P-ANULAR = Pressure in the well annulus [Pa] +P-JUS-BS = Downstream pressure of the SP (service pump) [Pa] +P-JUS-CKGL = Downstream pressure of the GLCK (gas lift choke) [Pa] +P-JUS-CKP = Downstream pressure of the PCK (production choke) [Pa] +P-MON-CKGL = Upstream pressure of the GLCK (gas lift choke) [Pa] +P-MON-CKP = Upstream pressure of the PCK (production choke) [Pa] +P-MON-SDV-P = Upstream pressure of the production SDV (shutdown valve) [Pa] +P-PDG = Pressure at the PDG (permanent downhole gauge) [Pa] +PT-P = Downstream pressure of the PWV (production wing valve) in the production tube [Pa] +P-TPT = Pressure at the TPT (temperature and pressure transducer) [Pa] +QBS = Flow rate at the SP (service pump) [m3/s] +QGL = Gas lift flow rate [m3/s] +T-JUS-CKP = Downstream temperature of the PCK (production choke) [oC] +T-MON-CKP = Upstream temperature of the PCK (production choke) [oC] +T-PDG = Temperature at the PDG (permanent downhole gauge) [oC] +T-TPT = Temperature at the TPT (temperature and pressure transducer) [oC] +class = Label of the observation +state = Well operational status + +# Common properties of all event types covered by the 3W Project +# +[EVENTS] +# Internal names of all event types +NAMES = NORMAL, ABRUPT_INCREASE_OF_BSW, SPURIOUS_CLOSURE_OF_DHSV, + SEVERE_SLUGGING, FLOW_INSTABILITY, RAPID_PRODUCTIVITY_LOSS, + QUICK_RESTRICTION_IN_PCK, SCALING_IN_PCK, + HYDRATE_IN_PRODUCTION_LINE, HYDRATE_IN_SERVICE_LINE +# Simulated and hand-drawn instances +EXTRA_INSTANCES_TRAINING = -1 +# Offset between an undesirable event's label and its transient label +TRANSIENT_OFFSET = 100 + +# This section defines default properties for a specific event type +# +[NORMAL] +LABEL = 0 +DESCRIPTION = Normal Operation + +# This section defines default properties for a specific event type +# +[ABRUPT_INCREASE_OF_BSW] +LABEL = 1 +DESCRIPTION = Abrupt Increase of BSW +TRANSIENT = True + +# This section defines default properties for a specific event type +# +[SPURIOUS_CLOSURE_OF_DHSV] +LABEL = 2 +DESCRIPTION = Spurious Closure of DHSV +TRANSIENT = True +WINDOW = 180 +STEP = 15 + +# This section defines default properties for a specific event type +# +[SEVERE_SLUGGING] +LABEL = 3 +DESCRIPTION = Severe Slugging +TRANSIENT = False + +# This section defines default properties for a specific event type +# +[FLOW_INSTABILITY] +LABEL = 4 +DESCRIPTION = Flow Instability +TRANSIENT = False + +# This section defines default properties for a specific event type +# +[RAPID_PRODUCTIVITY_LOSS] +LABEL = 5 +DESCRIPTION = Rapid Productivity Loss +TRANSIENT = True + +# This section defines default properties for a specific event type +# +[QUICK_RESTRICTION_IN_PCK] +LABEL = 6 +DESCRIPTION = Quick Restriction in PCK +TRANSIENT = True + +# This section defines default properties for a specific event type +# +[SCALING_IN_PCK] +LABEL = 7 +DESCRIPTION = Scaling in PCK +TRANSIENT = True + +# This section defines default properties for a specific event type +# +[HYDRATE_IN_PRODUCTION_LINE] +LABEL = 8 +DESCRIPTION = Hydrate in Production Line +TRANSIENT = True + +# This section defines default properties for a specific event type +# +[HYDRATE_IN_SERVICE_LINE] +LABEL = 9 +DESCRIPTION = Hydrate in Service Line +TRANSIENT = True + +# Settings related to the Parquet format +# +[PARQUET_SETTINGS] +# Used/chosen extension +PARQUET_EXTENSION = .parquet +# Used/chosen extension engine +PARQUET_ENGINE = pyarrow +# Used/chosen extension compression +PARQUET_COMPRESSION = brotli \ No newline at end of file diff --git a/bibmon/three_w/tools.py b/bibmon/three_w/tools.py new file mode 100644 index 0000000..c7d7bfd --- /dev/null +++ b/bibmon/three_w/tools.py @@ -0,0 +1,10 @@ +import configparser +import importlib.resources as pkg_resources +import os + + +def parse_dataset_ini(dataset_ini_path) -> configparser.ConfigParser: + config = configparser.ConfigParser() + config.read(dataset_ini_path) + + return config \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index d157d87..bf549e0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,5 @@ pandas>=2.2.2 matplotlib>=3.9.0 seaborn>=0.13.2 statsmodels>=0.14.1 -optuna>=3.6.1 \ No newline at end of file +optuna>=3.6.1 +pyarrow>=17.0.0 \ No newline at end of file From db3d9114eacfecedd8f9c4b676a6e356fd5b1102 Mon Sep 17 00:00:00 2001 From: Caracol Date: Tue, 15 Oct 2024 16:16:13 -0300 Subject: [PATCH 05/10] added split_dataset to 3w tools --- bibmon/__init__.py | 2 +- bibmon/_load_data.py | 110 ++++++++++++++++++++++------------------ bibmon/_preprocess.py | 2 +- bibmon/three_w/tools.py | 99 ++++++++++++++++++++++++++++++++++-- test/test_tools.py | 16 +++++- 5 files changed, 171 insertions(+), 58 deletions(-) diff --git a/bibmon/__init__.py b/bibmon/__init__.py index 373870a..1d783ab 100644 --- a/bibmon/__init__.py +++ b/bibmon/__init__.py @@ -9,7 +9,7 @@ __all__ = ['Autoencoder','PCA','ESN','SBM', 'sklearnRegressor', 'PreProcess', - 'load_tennessee_eastman', 'load_real_data', 'load_3_w', + 'load_tennessee_eastman', 'load_real_data', 'load_3w', 'train_val_test_split', 'complete_analysis', 'comparative_table', 'spearmanr_dendrogram', 'create_df_with_dates', 'create_df_with_noise', 'align_dfs_by_rows'] diff --git a/bibmon/_load_data.py b/bibmon/_load_data.py index fb481dd..35b54e0 100644 --- a/bibmon/_load_data.py +++ b/bibmon/_load_data.py @@ -8,7 +8,8 @@ ############################################################################### -def load_tennessee_eastman (train_id = 0, test_id = 0): + +def load_tennessee_eastman(train_id=0, test_id=0): """ Load the 'Tennessee Eastman Process' benchmark data. @@ -21,80 +22,82 @@ def load_tennessee_eastman (train_id = 0, test_id = 0): Identifier of the test data. No fault: 0. With faults: 1 to 20. Returns - ---------- + ---------- train_df: pandas.DataFrame Training data. test_df: pandas.DataFrame Test data. - """ - - tags1 = ['XMEAS('+str(ii)+')' for ii in range(1,42)] - tags2 = ['XMV('+str(ii)+')' for ii in range(1,12)] + """ + + tags1 = ["XMEAS(" + str(ii) + ")" for ii in range(1, 42)] + tags2 = ["XMV(" + str(ii) + ")" for ii in range(1, 12)] tags = tags1 + tags2 - - file_train = f'd{train_id}.dat' - file_test = f'd{test_id}_te.dat' + + file_train = f"d{train_id}.dat" + file_test = f"d{test_id}_te.dat" if len(file_train) == 6: - file_train = file_train[:2]+'0'+file_train[2:] - + file_train = file_train[:2] + "0" + file_train[2:] + if len(file_test) == 9: - file_test = file_test[:1]+'0'+file_test[1:] + file_test = file_test[:1] + "0" + file_test[1:] with pkg_resources.path(tennessee_eastman, file_train) as filepath: - if file_train == 'd00.dat': - - tmp1 = pd.read_csv(filepath,sep='\t', - names=['0']) - tmp2 = pd.DataFrame([tmp1.T.iloc[0,i].strip() for - i in range(tmp1.shape[0])]) + if file_train == "d00.dat": + + tmp1 = pd.read_csv(filepath, sep="\t", names=["0"]) + tmp2 = pd.DataFrame( + [tmp1.T.iloc[0, i].strip() for i in range(tmp1.shape[0])] + ) train_df = pd.DataFrame() for ii in range(52): - train_df[tags[ii]]=[float(s) for s in tmp2[0][ii].split(' ')] - - train_df = create_df_with_dates(train_df, - freq = '3min') - + train_df[tags[ii]] = [float(s) for s in tmp2[0][ii].split(" ")] + + train_df = create_df_with_dates(train_df, freq="3min") + else: - train_df = create_df_with_dates(pd.read_csv(filepath, - sep = '\s+', - names = tags), - freq = '3min') + train_df = create_df_with_dates( + pd.read_csv(filepath, sep="\s+", names=tags), freq="3min" + ) with pkg_resources.path(tennessee_eastman, file_test) as filepath: - test_df = create_df_with_dates(pd.read_csv(filepath, - sep = '\s+', - names = tags), - start = '2020-02-01 00:00:00', - freq = '3min') + test_df = create_df_with_dates( + pd.read_csv(filepath, sep="\s+", names=tags), + start="2020-02-01 00:00:00", + freq="3min", + ) return train_df, test_df + ############################################################################### -def load_real_data (): + +def load_real_data(): """ Load a sample of real process data. The variables have been anonymized for availability in the library. - + Returns - ---------- + ---------- : pandas.DataFrame Process data. - """ + """ + + with pkg_resources.path(real_process_data, "real_process_data.csv") as file: + return pd.read_csv(file, index_col=0, parse_dates=True) - with pkg_resources.path(real_process_data,'real_process_data.csv') as file: - return pd.read_csv(file,index_col = 0, parse_dates = True) ############################################################################### -AVAILABLE_3W_CLASSES = ['8'] +AVAILABLE_3W_CLASSES = ["8"] -def load_3w (dataset_class: Literal['8'] = '8'): + +def load_3w(dataset_class: Literal["8"] = "8"): """ Load the '3W-8' benchmark data. @@ -104,18 +107,25 @@ def load_3w (dataset_class: Literal['8'] = '8'): Identifier of the dataset class. Available classes: '8'. Returns - ---------- + ---------- : pandas.DataFrame Process data. + : configparser.ConfigParser + Configuration file. """ - if dataset_class != '8': - raise ValueError(f'Dataset class not available. Available classes: {AVAILABLE_3W_CLASSES}') - - with pkg_resources.path(three_w,'WELL-00019_20120601165020.parquet') as file: - with pkg_resources.path(three_w, 'dataset.ini') as path: - ini = three_w.tools.parse_dataset_ini(path) - return pd.read_parquet( - file, - engine=ini.get("PARQUET_SETTINGS", "PARQUET_ENGINE"), + if dataset_class != "8": + raise ValueError( + f"Dataset class not available. Available classes: {AVAILABLE_3W_CLASSES}" + ) + + with pkg_resources.path(three_w, "WELL-00019_20120601165020.parquet") as file: + with pkg_resources.path(three_w, "dataset.ini") as path: + ini = three_w.tools.load_dataset_ini(path) + return ( + pd.read_parquet( + file, + engine=ini.get("PARQUET_SETTINGS", "PARQUET_ENGINE"), + ), + ini, ) diff --git a/bibmon/_preprocess.py b/bibmon/_preprocess.py index 0a85adb..f7fad6a 100644 --- a/bibmon/_preprocess.py +++ b/bibmon/_preprocess.py @@ -81,7 +81,7 @@ def a_pp(self, a_pp): ########################################################################### - def apply(self, df, train_or_test = 'train'): + def apply(self, df, train_or_test = 'train') -> pd.DataFrame: """ Sequentially applies the preprocessing functions defined during initialization. diff --git a/bibmon/three_w/tools.py b/bibmon/three_w/tools.py index c7d7bfd..af84349 100644 --- a/bibmon/three_w/tools.py +++ b/bibmon/three_w/tools.py @@ -1,10 +1,99 @@ import configparser -import importlib.resources as pkg_resources -import os +import pandas as pd +import bibmon._preprocess as preproc +import bibmon._bibmon_tools as b_tools +from typing import Literal, Tuple -def parse_dataset_ini(dataset_ini_path) -> configparser.ConfigParser: +def load_dataset_ini(dataset_ini_path) -> configparser.ConfigParser: + """ + Loads the dataset.ini file. + + Parameters + ---------- + dataset_ini_path: string + Path to the dataset.ini file. + Returns + ---------- + : configparser.ConfigParser + Configuration file. + """ + config = configparser.ConfigParser() config.read(dataset_ini_path) - - return config \ No newline at end of file + + return config + + +############################################################################### + + +def split_dataset( + DataFrame: pd.DataFrame, + config_file: configparser.ConfigParser, + training_percentage: float = 0.8, + validation_percentage: float = 0.2, + split_on: Literal["transition_state", "error_state"] = "transition_state", +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Splits a DataFrame into training, validation and test sets. + + The split is done according to the transition_state or error_state, the sum of training and validation percentages must equal 1. + + Parameters + ---------- + DataFrame: pandas.DataFrame + Data to be split. + config_file: configparser.ConfigParser + Configuration file. + training_percentage: float + Percentage of the training set. + validation_percentage: float + Percentage of the validation set. + split_on: string + Split the data on the transition_state or error_state. + Returns + ---------- + : tuple of pandas.DataFrames + Training, validation and test sets. + """ + + if training_percentage + validation_percentage != 1: + raise ValueError( + "The sum of training and validation percentages must equals 1." + ) + + df_processed = preproc.PreProcess(f_pp=["ffill_nan"]).apply(DataFrame) + + transitions = b_tools.find_df_transitions(df_processed, 1, "number", "class") + + split_index = 0 + + for t in transitions: + if t == 0: + continue + + transition_class = int(df_processed.iloc[t]["class"]) + transient_offset = int(config_file.get("EVENTS", "TRANSIENT_OFFSET")) + + if transition_class > transient_offset and split_on == "transition_state": + split_index = t + break + if transition_class < transient_offset and split_on == "error_state": + split_index = t + break + + if split_index == 0: + raise ValueError("No split index found.") + + train_and_validation_df = df_processed.iloc[:split_index] + test_df = df_processed.iloc[split_index:] + + splitted_df = b_tools.split_df_percentages( + train_and_validation_df, [training_percentage, validation_percentage] + ) + + train_df = splitted_df[0] + validation_df = splitted_df[1] + + return train_df, validation_df, test_df diff --git a/test/test_tools.py b/test/test_tools.py index db21c1b..071bd33 100644 --- a/test/test_tools.py +++ b/test/test_tools.py @@ -9,6 +9,8 @@ import bibmon import pandas as pd +import bibmon.three_w + def test_complete_analysis(): # load data @@ -85,4 +87,16 @@ def test_split_df_percentages_error(): try: _ = bibmon._bibmon_tools.split_df_percentages(data, [0.6, 0.2]) except ValueError: - assert True \ No newline at end of file + assert True + +def test_3w_load_dataset_ini(): + config = bibmon.three_w.tools.load_dataset_ini("dataset.ini") + + assert config["VERSION"]["DATASET"] == "2.0.0" + +def test_3w_split_dataset(): + data, conf = bibmon.load_3w() + + train_df, validation_df, test_df = bibmon.three_w.tools.split_dataset(data, conf) + + assert (train_df.shape[0], validation_df.shape[0], test_df.shape[0]) == (85112, 21278, 136746) \ No newline at end of file From f016640f1639a00712a34cff0f42092c44d713f8 Mon Sep 17 00:00:00 2001 From: "Rafael F. Meneses" <107453198+zRafaF@users.noreply.github.com> Date: Tue, 15 Oct 2024 19:17:42 -0300 Subject: [PATCH 06/10] Fix relative imports (#1) * fixed relative imports * fixed relative imports * removed debbug prints --- bibmon/_load_data.py | 8 ++++---- bibmon/three_w/tools.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/bibmon/_load_data.py b/bibmon/_load_data.py index 35b54e0..bcb2f3b 100644 --- a/bibmon/_load_data.py +++ b/bibmon/_load_data.py @@ -3,7 +3,7 @@ import importlib.resources as pkg_resources from typing import Literal -from ._bibmon_tools import create_df_with_dates +from . import _bibmon_tools as b_tools from . import real_process_data, tennessee_eastman, three_w ############################################################################### @@ -55,17 +55,17 @@ def load_tennessee_eastman(train_id=0, test_id=0): for ii in range(52): train_df[tags[ii]] = [float(s) for s in tmp2[0][ii].split(" ")] - train_df = create_df_with_dates(train_df, freq="3min") + train_df = b_tools.create_df_with_dates(train_df, freq="3min") else: - train_df = create_df_with_dates( + train_df = b_tools.create_df_with_dates( pd.read_csv(filepath, sep="\s+", names=tags), freq="3min" ) with pkg_resources.path(tennessee_eastman, file_test) as filepath: - test_df = create_df_with_dates( + test_df = b_tools.create_df_with_dates( pd.read_csv(filepath, sep="\s+", names=tags), start="2020-02-01 00:00:00", freq="3min", diff --git a/bibmon/three_w/tools.py b/bibmon/three_w/tools.py index af84349..a73ced2 100644 --- a/bibmon/three_w/tools.py +++ b/bibmon/three_w/tools.py @@ -1,7 +1,7 @@ import configparser import pandas as pd -import bibmon._preprocess as preproc -import bibmon._bibmon_tools as b_tools +from .. import _preprocess as preproc +from .. import _bibmon_tools as b_tools from typing import Literal, Tuple From 393ff8302a4af674cad05732093dc509a1724f66 Mon Sep 17 00:00:00 2001 From: Reinaldo-Kn Date: Wed, 16 Oct 2024 11:09:50 -0300 Subject: [PATCH 07/10] added grid search --- bibmon/_gridsearch.py | 233 ++++++++++++++++++++++++++++++++++++++++++ bibmon/_preprocess.py | 2 +- 2 files changed, 234 insertions(+), 1 deletion(-) create mode 100644 bibmon/_gridsearch.py diff --git a/bibmon/_gridsearch.py b/bibmon/_gridsearch.py new file mode 100644 index 0000000..24d5ff5 --- /dev/null +++ b/bibmon/_gridsearch.py @@ -0,0 +1,233 @@ +from sklearn.model_selection import train_test_split, GridSearchCV +from sklearn.ensemble import RandomForestRegressor +import pandas as pd +import joblib +import matplotlib.pyplot as plt +import numpy as np +from sklearn.model_selection import cross_val_score + +class GridSearchModel: + """ + Class used to encapsulate the grid search process for hyperparameter tuning. + + Parameters + ---------- + model : estimator object + The base model to perform grid search on (e.g., RandomForestRegressor). + + param_grid : dict, optional + The grid of parameters to search over. If not provided, a default + parameter grid for RandomForestRegressor will be used. + + scoring : string, optional + The scoring method to use for evaluating the models. + + cv : int, optional + Number of cross-validation folds. + + test_size : float, optional + The percentage of data used for testing, default is 0.1 (10%). + + Attributes + ---------- + best_params_ : dict + The best parameters found by the grid search. + + best_estimator_ : estimator object + The estimator that was chosen by the grid search. + + Methods + ---------- + fit(df, target_column): + Fits the grid search using the provided data. + """ + + def __init__(self, model=None, param_grid=None, scoring='neg_mean_absolute_error', cv=5, test_size=0.01): + self.model = model if model is not None else RandomForestRegressor(n_jobs=-1, random_state=42) + + # Param_grid default + default_param_grid = { + 'n_estimators': [10, 100], + 'max_depth': [None, 10], + 'min_samples_split': [2, 4], + } + self.param_grid = param_grid if param_grid is not None else default_param_grid + + self.scoring = scoring + self.cv = cv + self.test_size = test_size # Using only 10% of the data for testing + self.grid_search = None + self.best_params_ = None + self.best_estimator_ = None + + def fit(self, df: pd.DataFrame, target_column: str): + """ + Fit the grid search model on the provided data. + + Parameters + ---------- + df : pandas.DataFrame + The dataset including features and target column. + + target_column : str + The name of the column to be predicted (target variable). + """ + X = df.drop(columns=[target_column]) + Y = df[target_column] + + + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=self.test_size, random_state=42) + + self.grid_search = GridSearchCV( + estimator=self.model, + param_grid=self.param_grid, + scoring=self.scoring, + cv=self.cv, + n_jobs=-1 + ) + + self.grid_search.fit(X_train, Y_train) + + self.best_params_ = self.grid_search.best_params_ + self.best_estimator_ = self.grid_search.best_estimator_ + + return self.best_estimator_ + + def predict(self, X): + """ + Make predictions using the best estimator found by grid search. + + Parameters + ---------- + X : pandas.DataFrame or numpy.ndarray + Input data for making predictions. + + Returns + ---------- + y_pred : numpy.ndarray + Predicted values. + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + return self.best_estimator_.predict(X) + + def score(self, X_test, y_test): + """ + Evaluate the best estimator's performance on the test set. + + Parameters + ---------- + X_test : pandas.DataFrame or numpy.ndarray + Test features. + y_test : pandas.Series or numpy.ndarray + True values for the test set. + + Returns + ---------- + score : float + The score of the best model on the test set based on the defined scoring metric. + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + return self.best_estimator_.score(X_test, y_test) + + def get_best_params(self): + """ + Get the best hyperparameters found by grid search. + + Returns + ---------- + best_params : dict + Dictionary of the best hyperparameters. + """ + if self.best_params_ is None: + raise ValueError("Grid search has not been performed yet. Call the `fit` method first.") + return self.best_params_ + + + def save_model(self, filename): + """ + Save the best model to a file. + + Parameters + ---------- + filename : str + Path where the model should be saved. + + Returns + ---------- + None + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + joblib.dump(self.best_estimator_, filename) + + def load_model(self, filename): + """ + Load a model from a file. + + Parameters + ---------- + filename : str + Path to the saved model file. + + Returns + ---------- + None + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + self.best_estimator_ = joblib.load(filename) + + + def plot_feature_importance(self, feature_names): + """ + Plot the feature importance from the best estimator. + + Parameters + ---------- + feature_names : list + List of feature names in the same order as they appear in the dataset. + + Returns + ---------- + None + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + + importances = self.best_estimator_.feature_importances_ + indices = np.argsort(importances)[::-1] + + plt.figure(figsize=(10, 6)) + plt.title("Feature Importance") + plt.bar(range(len(importances)), importances[indices], align='center') + plt.xticks(range(len(importances)), [feature_names[i] for i in indices], rotation=90) + plt.tight_layout() + plt.show() + + def cross_val_score_summary(self, X, Y): + """ + Get a summary of cross-validation scores for the best model. + + Parameters + ---------- + X : pandas.DataFrame + Input data (features). + Y : pandas.Series + Target variable. + + Returns + ---------- + summary : dict + Dictionary with mean and standard deviation of cross-validation scores. + """ + if self.best_estimator_ is None: + raise ValueError("The model has not been fitted yet. Call the `fit` method first.") + + scores = cross_val_score(self.best_estimator_, X, Y, cv=self.cv, scoring=self.scoring) + return { + 'mean_score': scores.mean(), + 'std_dev': scores.std(), + 'scores': scores + } \ No newline at end of file diff --git a/bibmon/_preprocess.py b/bibmon/_preprocess.py index f7fad6a..22c0528 100644 --- a/bibmon/_preprocess.py +++ b/bibmon/_preprocess.py @@ -1,5 +1,5 @@ import copy -import pandas as pd +import pandas as pd import statsmodels.tsa.tsatools ############################################################################### From 0e5ec7a573755614e077262eca86406c80d20066 Mon Sep 17 00:00:00 2001 From: Reinaldo-Kn Date: Thu, 17 Oct 2024 14:05:54 -0300 Subject: [PATCH 08/10] added more func for preprocess --- bibmon/_gridsearch.py | 28 +++++++++- bibmon/_preprocess.py | 116 ++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 139 insertions(+), 5 deletions(-) diff --git a/bibmon/_gridsearch.py b/bibmon/_gridsearch.py index 24d5ff5..08f8a62 100644 --- a/bibmon/_gridsearch.py +++ b/bibmon/_gridsearch.py @@ -5,6 +5,7 @@ import matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import cross_val_score +import xgboost as xgb class GridSearchModel: """ @@ -230,4 +231,29 @@ def cross_val_score_summary(self, X, Y): 'mean_score': scores.mean(), 'std_dev': scores.std(), 'scores': scores - } \ No newline at end of file + } + + def xgboost_best(self, df: pd.DataFrame, target_column: str): + """Perform grid search to find the best parameters for XGBRegressor.""" + X = df.drop(columns=[target_column]) + Y = df[target_column] + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=self.test_size, random_state=42) + + param_grid = { + 'n_estimators': [100, 200], + 'learning_rate': [ 0.05, 0.1], + 'max_depth': [None, 6], + 'subsample': [0.1,0.5,], + 'colsample_bytree': [0.1,0.5], + 'gamma': [0, 0.1], + 'reg_alpha': [0, 0.1], + 'reg_lambda': [0.1, 0.5] + } + + model = xgb.XGBRegressor(tree_method='gpu_hist', predictor='gpu_predictor', random_state=42) + self.grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring=self.scoring, cv=self.cv, n_jobs=-1) + self.grid_search.fit(X_train, Y_train) + self.best_params_ = self.grid_search.best_params_ + self.best_estimator_ = self.grid_search.best_estimator_ + + return self.best_estimator_ \ No newline at end of file diff --git a/bibmon/_preprocess.py b/bibmon/_preprocess.py index 22c0528..45890e8 100644 --- a/bibmon/_preprocess.py +++ b/bibmon/_preprocess.py @@ -1,7 +1,7 @@ import copy -import pandas as pd +import pandas as pd import statsmodels.tsa.tsatools - +import numpy as np ############################################################################### class PreProcess (): @@ -81,7 +81,7 @@ def a_pp(self, a_pp): ########################################################################### - def apply(self, df, train_or_test = 'train') -> pd.DataFrame: + def apply(self, df, train_or_test = 'train'): """ Sequentially applies the preprocessing functions defined during initialization. @@ -391,4 +391,112 @@ def moving_average_filter (self, df, train_or_test = 'train', WS = 10): if hasattr(df,'name'): new_df.name = df.name - return new_df.drop(df.index[:WS]) \ No newline at end of file + return new_df.drop(df.index[:WS]) + + def time_based_windowing(self, df, train_or_test='train', window_size='30min', agg_func= "max"): + """ + Applies a rolling window based on time, where each window is determined by the timestamp index. + + Parameters + ---------- + df: pandas.DataFrame + Data to be processed. + train_or_test: string, optional + Indicates which step the data corresponds to. + window_size: string, optional + Size of the time window (e.g., '30min' for 30 minutes, '1H' for 1 hour). + agg_func: string, optional + Aggregation function to apply within each window ('mean', 'sum', 'max', 'min', etc.). + + Returns + ---------- + : pandas.DataFrame + Processed data. + """ + + if not pd.api.types.is_datetime64_any_dtype(df.index): + raise ValueError("DataFrame index must be a datetime type.") + + if agg_func == 'mean': + return df.resample(window_size).mean() + elif agg_func == 'sum': + return df.resample(window_size).sum() + elif agg_func == 'max': + return df.resample(window_size).max() + elif agg_func == 'min': + return df.resample(window_size).min() + else: + raise ValueError("Unsupported aggregation function. Choose from 'mean', 'sum', 'max', or 'min'.") + + + def remove_constant_difference(self, df,train_or_test='train'): + """ + Calculate the cumulative sum. + Drop columns with constant difference. + + Parameters + ---------- + df: pandas.DataFrame + Data to be processed. + train_or_test: string, optional + Indicates which step the data corresponds to. + + Returns + ------- + pandas.DataFrame + Processed DataFrame without columns containing constant difference. + """ + cumulative_df = df.cumsum() + columns_to_drop = [] + + for column in cumulative_df.columns: + if not pd.api.types.is_numeric_dtype(cumulative_df[column]): + raise TypeError(f"'{column}' is not numeric type.") + + diff = cumulative_df[column].diff() + + # Verify constant difference + if len(diff.dropna()) > 1 and diff.nunique() == 1: + columns_to_drop.append(column) + + cumulative_df.drop(columns=columns_to_drop, inplace=True) + return cumulative_df + + def differencing(self, df, train_or_test='train', lag=1): + """ + Apply differencing to remove trend or seasonality. + + Parameters + ---------- + df: pandas.DataFrame + Data to be processed. + train_or_test: string, optional + Indicates which step the data corresponds to. + lag: int, optional + Lag to be used for differencing. + Returns + ---------- + : pandas.DataFrame + Processed data. + """ + return df.diff(periods=lag).dropna() + + + + def log_transform(self, df, train_or_test='train'): + """ + Apply log transformation to reduce variability. + + Parameters + ---------- + df: pandas.DataFrame + Data to be processed. + train_or_test: string, optional + Indicates which step the data corresponds to. + + Returns + ---------- + : pandas.DataFrame + Processed data. + """ + return df.apply(lambda x: np.log1p(x)) \ No newline at end of file From 6a9fd8f596d93f5a8c3b29ed87eac247108ed384 Mon Sep 17 00:00:00 2001 From: Reinaldo-Kn Date: Fri, 18 Oct 2024 00:19:00 -0300 Subject: [PATCH 09/10] more func for alarms detection --- bibmon/_alarms.py | 147 +++++++++++++++++++++++++++++++++++++++ bibmon/_xgboost_model.py | 89 ++++++++++++++++++++++++ 2 files changed, 236 insertions(+) create mode 100644 bibmon/_xgboost_model.py diff --git a/bibmon/_alarms.py b/bibmon/_alarms.py index dea0af9..895b166 100644 --- a/bibmon/_alarms.py +++ b/bibmon/_alarms.py @@ -44,3 +44,150 @@ def detecOutlier(data, lim, count = False, count_limit = 1): alarm = +1 return alarm +def detectStdDevOutlier(data, threshold=2): + """ + Detects outliers based on the standard deviation method. + + Parameters + ---------- + data: array-like + The input data to be analyzed. + threshold: float, optional + The number of standard deviations from the mean to consider + as an outlier. Default is 2. + + Returns + ---------- + alarm: ndarray + An array indicating the outliers (0 for values within + threshold * stddev, 1 for outliers). + """ + mean = np.nanmean(data) + std_dev = np.nanstd(data) + upper_limit = mean + threshold * std_dev + lower_limit = mean - threshold * std_dev + return np.where((data > upper_limit) | (data < lower_limit), 1, 0) + + +def detectTrend(data, window_size=5): + """ + Detects trend in the data using a moving average. + + Parameters + ---------- + data: array-like + The input data to be analyzed. + window_size: int + The size of the moving window to calculate the trend. + + Returns + ---------- + alarm: ndarray + An array indicating the trend (1 for positive trend, -1 for negative trend, 0 for no trend). + """ + if len(data) < window_size: + return np.zeros(len(data)) + + moving_avg = np.convolve(data, np.ones(window_size)/window_size, mode='valid') + trend = np.diff(moving_avg) + trend_alarm = np.where(trend > 0, 1, np.where(trend < 0, -1, 0)) + + # Pad the result with zeros for the beginning of the array + trend_alarm = np.pad(trend_alarm, (window_size-1, 0), mode='constant') + + return trend_alarm + + +def detectMovingWindowOutlier(data, window_size=10, count_limit=1): + """ + Detects outliers in a moving window. + + Parameters + ---------- + data: array-like + The input data to be analyzed. + window_size: int + The size of the moving window to analyze. + count_limit: int + The maximum number of outliers allowed within the window. + + Returns + ---------- + alarm: ndarray + An array indicating if the count of outliers exceeds + the count_limit within each window (1 for alarm, 0 for no alarm). + """ + alarm = np.zeros(len(data)) + for i in range(len(data) - window_size + 1): + window = data[i:i + window_size] + if np.count_nonzero(window > np.nanmean(window)) > count_limit: + alarm[i + window_size - 1] = 1 # Mark the last element in the window + return alarm + +def detectBias(data, expected_value, threshold=0.1): + """ + Detects bias in the data by comparing the mean to an expected value. + + Parameters + ---------- + data: array-like + The input data to be analyzed. + expected_value: float + The expected mean value to compare against. + threshold: float, optional + The threshold for deviation from the expected value. Default is 0.1. + + Returns + ---------- + alarm: ndarray + An array indicating if the bias exceeds the threshold (1 for alarm, 0 for no alarm). + """ + mean_value = np.nanmean(data) + return np.where(np.abs(mean_value - expected_value) > threshold, 1, 0) + + +def detectNelsonRules(data,threshold=1): + """ + Detects anomalies in the data based on Nelson Rules 1, 2, and 3. + + Parameters + ---------- + data: array-like + The input data to be analyzed. + + Returns + ---------- + alarms: dict + A dictionary with alarms for each rule (1 for alarm, 0 for no alarm). + """ + mean_value = np.nanmean(data) + std_dev = np.nanstd(data) + + #rule 1 = 1 point exceeding the threshold + rule_1_alarms = np.where(np.abs(data - mean_value) > threshold * std_dev, 1, 0) + + #rule 2 = 2 points in a row exceeding the threshold + rule_2_alarms = np.zeros_like(data) + for i in range(1, len(data)): + if (np.abs(data[i] - mean_value) > threshold * std_dev) and (np.abs(data[i-1] - mean_value) > threshold * std_dev): + rule_2_alarms[i-1:i+1] = 1 + + # rule 3 = 6 points in a row increasing or decreasing + rule_3_alarms = np.zeros_like(data) + count = 0 + for i in range(len(data)): + if data[i] > mean_value + threshold : + count += 1 + if count >= 6: + rule_3_alarms[i-5:i+1] = 1 + else: + count = 0 + alarms = { + 'rule_1': rule_1_alarms, + 'rule_2': rule_2_alarms, + 'rule_3': rule_3_alarms + } + + return alarms + + diff --git a/bibmon/_xgboost_model.py b/bibmon/_xgboost_model.py new file mode 100644 index 0000000..be70e51 --- /dev/null +++ b/bibmon/_xgboost_model.py @@ -0,0 +1,89 @@ +from xgboost import XGBRegressor +from _generic_model import GenericModel # Assuming GenericModel is in the _generic_model.py file +from sklearn.metrics import r2_score + +class XGBModel(GenericModel): + def __init__(self, model_params=None, *args, **kwargs): + """ + Initializes the XGBModel class, which is a subclass of GenericModel. + + Parameters + ---------- + model_params: dict, optional + Parameters for the XGBRegressor to be used. If not provided, + the default values of XGBRegressor will be used. + """ + super().__init__(*args, **kwargs) + if model_params is None: + model_params = { + 'n_estimators': 100, + 'learning_rate': 0.1, + 'max_depth': 3, + 'subsample': 0.8, + 'colsample_bytree': 0.8, + 'random_state': 42 + } + # Setting the model as XGBRegressor with the provided parameters + self.model = XGBRegressor(**model_params) + + def train(self, X_train, Y_train, **kwargs): + """ + Trains the XGBRegressor model on the provided data. + + Parameters + ---------- + X_train: pandas.DataFrame or numpy.ndarray + Input dataset for training. + Y_train: pandas.DataFrame or numpy.ndarray + Target labels or values for training. + + Returns + ------- + str + Confirmation message that training is completed. + """ + self.model.fit(X_train, Y_train, **kwargs) + return "Training completed." + + def predict(self, X_test): + """ + Makes predictions with the XGBRegressor model on the provided test data. + + Parameters + ---------- + X_test: pandas.DataFrame or numpy.ndarray + Input dataset to make predictions. + + Returns + ------- + numpy.ndarray + Predicted values by the model. + """ + Y_pred = self.model.predict(X_test) + return Y_pred + + def evaluate(self, X_test, Y_test, metric_func=None): + """ + Evaluates the model's performance on the test data. + + Parameters + ---------- + X_test: pandas.DataFrame or numpy.ndarray + Input dataset for evaluation. + Y_test: pandas.DataFrame or numpy.ndarray + Target labels or values for evaluation. + metric_func: callable, optional + Metric function to be used to evaluate the model. If not provided, uses R2 score. + + Returns + ------- + float + Result of the evaluation metric. + """ + Y_pred = self.predict(X_test) + if metric_func is None: + + score = r2_score(Y_test, Y_pred) + else: + score = metric_func(Y_test, Y_pred) + return score From 2e2a0c2abcee5777f4ac40474f4ba2d4b973ee5e Mon Sep 17 00:00:00 2001 From: Reinaldo-Kn Date: Fri, 18 Oct 2024 11:06:58 -0300 Subject: [PATCH 10/10] new ways to search for the best columns to train your model --- bibmon/_best_columns.py | 130 +++++++++++++++++++++++++++++++++++++++ bibmon/_xgboost_model.py | 89 --------------------------- 2 files changed, 130 insertions(+), 89 deletions(-) create mode 100644 bibmon/_best_columns.py delete mode 100644 bibmon/_xgboost_model.py diff --git a/bibmon/_best_columns.py b/bibmon/_best_columns.py new file mode 100644 index 0000000..aef02ef --- /dev/null +++ b/bibmon/_best_columns.py @@ -0,0 +1,130 @@ +import pandas as pd +import xgboost as xgb +from sklearn.model_selection import train_test_split +from sklearn.metrics import r2_score, mean_absolute_error +from sklearn.feature_selection import mutual_info_regression + + +def bestColumn_pearson_spearman(df): + """ + This function calculates the Pearson and Spearman correlation coefficients + between all columns in the dataframe. It identifies the column with the highest + average correlation (positive) with other columns using both correlation methods. + + Pearson measures linear relationships, while Spearman measures monotonic relationships. + + Returns: + dict: A dictionary containing the best column for each correlation method. + """ + pearson_corr = df.corr(method="pearson") + spearman_corr = df.corr(method="spearman") + + # Calculate the average correlation of each column with the others + spearman_correlation_mean = spearman_corr.mean().sort_values(ascending=False) + y_column_spearman = spearman_correlation_mean.index[1] + + peasron_correlation_mean = pearson_corr.mean().sort_values(ascending=False) + y_column_pearson = peasron_correlation_mean.index[1] + + #return a dictionary with the best column for each correlation method + return {'pearson': y_column_pearson, 'spearman': y_column_spearman} + +def bestColumn_with_least_mae_or_r2(df): + """ + This function evaluates each column as a target variable for regression + and calculates the Mean Absolute Error (MAE) and R-squared (R²) scores + for predictions made by an XGBoost regressor. + + It identifies which column minimizes MAE and maximizes R², providing insights + on the best target variable based on predictive performance. + + Returns: + dict: A dictionary with sorted results for MAE and R². + """ + performance_mae = {} + performance_r2 = {} + + for columns in df.columns: + X = df.drop(columns, axis=1) + Y = df[columns] + + Y = Y.fillna(Y.mean()) + + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42) + + #using XGBRegressor to predict the column, because it is faster than the other regressors that dosent not use GPU + model = xgb.XGBRegressor(random_state=42, n_jobs=-1, max_depth=None, tree_method='gpu_hist', predictor='gpu_predictor') + model.fit(X_train, Y_train) + + Y_pred = model.predict(X_test) + + mae = mean_absolute_error(Y_test, Y_pred) + r2 = r2_score(Y_test, Y_pred) + + performance_mae[columns] = mae + performance_r2[columns] = r2 + + + performance_mae_sorted = sorted(performance_mae.items(), key=lambda x: x[1], reverse=False) + performance_r2_sorted = sorted(performance_r2.items(), key=lambda x: x[1], reverse=True) + + return {'r2': performance_r2_sorted,'mae': performance_mae_sorted} + +def bestColumn_feature_importance(df): + + """ + This function evaluates the importance of each feature by training an XGBoost regressor + for each column and computing the average feature importance. This helps to identify + which columns contribute most to predicting the target variable. + + Returns: + list: A sorted list of feature importances for each column. + """ + + feature_importances = {} + + for columns in df.columns: + X = df.drop(columns, axis=1) + Y = df[columns] + + Y = Y.fillna(Y.mean()) + + X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42) + + model = xgb.XGBRegressor(random_state=42, n_jobs=-1, max_depth=None, tree_method='gpu_hist', predictor='gpu_predictor') + model.fit(X_train, Y_train) + + # Importance of the features + feature_importance = model.feature_importances_ + feature_importances[columns] = feature_importance.mean() + + feature_importances_sorted = sorted(feature_importances.items(), key=lambda x: x[1], reverse=True) + + return feature_importances_sorted + + +def bestColumn_mutual_information(df): + + """ + This function calculates the mutual information scores between each column + and the other columns in the dataframe. Mutual information quantifies the + amount of information obtained about one variable through the other, helping + to determine which features are most informative for predicting the target variable. + + Returns: + list: A sorted list of mutual information scores for each column. + """ + + mi_scores = {} + + for columns in df.columns: + X = df.drop(columns, axis=1) + Y = df[columns] + + mi = mutual_info_regression(X, Y) + mi_scores[columns] = mi.mean() # + + + mi_sorted = sorted(mi_scores.items(), key=lambda x: x[1], reverse=True) + + return mi_sorted \ No newline at end of file diff --git a/bibmon/_xgboost_model.py b/bibmon/_xgboost_model.py deleted file mode 100644 index be70e51..0000000 --- a/bibmon/_xgboost_model.py +++ /dev/null @@ -1,89 +0,0 @@ -from xgboost import XGBRegressor -from _generic_model import GenericModel # Assuming GenericModel is in the _generic_model.py file -from sklearn.metrics import r2_score - -class XGBModel(GenericModel): - def __init__(self, model_params=None, *args, **kwargs): - """ - Initializes the XGBModel class, which is a subclass of GenericModel. - - Parameters - ---------- - model_params: dict, optional - Parameters for the XGBRegressor to be used. If not provided, - the default values of XGBRegressor will be used. - """ - super().__init__(*args, **kwargs) - if model_params is None: - model_params = { - 'n_estimators': 100, - 'learning_rate': 0.1, - 'max_depth': 3, - 'subsample': 0.8, - 'colsample_bytree': 0.8, - 'random_state': 42 - } - # Setting the model as XGBRegressor with the provided parameters - self.model = XGBRegressor(**model_params) - - def train(self, X_train, Y_train, **kwargs): - """ - Trains the XGBRegressor model on the provided data. - - Parameters - ---------- - X_train: pandas.DataFrame or numpy.ndarray - Input dataset for training. - Y_train: pandas.DataFrame or numpy.ndarray - Target labels or values for training. - - Returns - ------- - str - Confirmation message that training is completed. - """ - self.model.fit(X_train, Y_train, **kwargs) - return "Training completed." - - def predict(self, X_test): - """ - Makes predictions with the XGBRegressor model on the provided test data. - - Parameters - ---------- - X_test: pandas.DataFrame or numpy.ndarray - Input dataset to make predictions. - - Returns - ------- - numpy.ndarray - Predicted values by the model. - """ - Y_pred = self.model.predict(X_test) - return Y_pred - - def evaluate(self, X_test, Y_test, metric_func=None): - """ - Evaluates the model's performance on the test data. - - Parameters - ---------- - X_test: pandas.DataFrame or numpy.ndarray - Input dataset for evaluation. - Y_test: pandas.DataFrame or numpy.ndarray - Target labels or values for evaluation. - metric_func: callable, optional - Metric function to be used to evaluate the model. If not provided, uses R2 score. - - Returns - ------- - float - Result of the evaluation metric. - """ - Y_pred = self.predict(X_test) - if metric_func is None: - - score = r2_score(Y_test, Y_pred) - else: - score = metric_func(Y_test, Y_pred) - return score

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