Skip to content
Snippets Groups Projects
Select Git revision
  • enxhi_issue460_remove_TOAR-I_access
  • michael_issue459_preprocess_german_stations
  • sh_pollutants
  • develop protected
  • master default protected
  • release_v2.4.0
  • michael_issue450_feat_load-ifs-data
  • lukas_issue457_feat_set-config-paths-as-parameter
  • lukas_issue454_feat_use-toar-statistics-api-v2
  • lukas_issue453_refac_advanced-retry-strategy
  • lukas_issue452_bug_update-proj-version
  • lukas_issue449_refac_load-era5-data-from-toar-db
  • lukas_issue451_feat_robust-apriori-estimate-for-short-timeseries
  • lukas_issue448_feat_load-model-from-path
  • lukas_issue447_feat_store-and-load-local-clim-apriori-data
  • lukas_issue445_feat_data-insight-plot-monthly-distribution
  • lukas_issue442_feat_bias-free-evaluation
  • lukas_issue444_feat_choose-interp-method-cams
  • 414-include-crps-analysis-and-other-ens-verif-methods-or-plots
  • lukas_issue384_feat_aqw-data-handler
  • v2.4.0 protected
  • v2.3.0 protected
  • v2.2.0 protected
  • v2.1.0 protected
  • Kleinert_etal_2022_initial_submission
  • v2.0.0 protected
  • v1.5.0 protected
  • v1.4.0 protected
  • v1.3.0 protected
  • v1.2.1 protected
  • v1.2.0 protected
  • v1.1.0 protected
  • IntelliO3-ts-v1.0_R1-submit
  • v1.0.0 protected
  • v0.12.2 protected
  • v0.12.1 protected
  • v0.12.0 protected
  • v0.11.0 protected
  • v0.10.0 protected
  • IntelliO3-ts-v1.0_initial-submit
40 results

mlair

  • Clone with SSH
  • Clone with HTTPS
  • user avatar
    Felix Kleinert authored
    895c81e8
    History

    MLAir - Machine Learning on Air Data

    MLAir (Machine Learning on Air data) is an environment that simplifies and accelerates the creation of new machine learning (ML) models for the analysis and forecasting of meteorological and air quality time series. You can find the docs here.

    Installation

    MLAir is based on several python frameworks. To work properly, you have to install all packages from the requirements.txt file. Additionally to support the geographical plotting part it is required to install geo packages built for your operating system. Unfortunately, the names of these package may differ for different systems. In this instruction, we try to address users of different operating systems namely openSUSE Leap, Ubuntu and macOS. If the installation is still not working, we recommend skipping the geographical plot. We have put together a small workaround here. For special instructions to install MLAir on the Juelich HPC systems, see here.

    • Make sure to have the python3.6 version installed.
    • (geo) A c++ compiler is required for the installation of the program cartopy
    • (geo) Install proj and GEOS on your machine using the console.
    • Install the python3.6 develop libraries.
    • Install all requirements from requirements.txt preferably in a virtual environment. You can use pip install -r requirements.txt to install all requirements at once. Note, we recently updated the version of Cartopy and there seems to be an ongoing issue when installing numpy and Cartopy at the same time. If you run into trouble, you could use cat requirements.txt | cut -f1 -d"#" | sed '/^\s*$/d' | xargs -L 1 pip install instead.
    • Installation of MLAir:
      • Either clone MLAir from the gitlab repository and use it without installation (beside the requirements)
      • or download the distribution file (current version) and install it via pip install <dist_file>.whl. In this case, you can simply import MLAir in any python script inside your virtual environment using import mlair.
    • (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't find any compatibility errors. Please note, that tf-1.13 and 1.15 have two distinct branches each, the default branch for CPU support, and the "-gpu" branch for GPU support. If the GPU version is installed, MLAir will make use of the GPU device.

    openSUSE Leap 15.1

    • c++ compiler

    sudo zypper install gcc-c++

    • geo packages

    sudo zypper install proj geos-devel

    • depending on the pre-installed packages it could be required to install further packages

    sudo zypper install libproj-devel binutils gdal-devel graphviz

    • python develop libraries

    sudo zypper install python3-devel

    Ubuntu 20.04.1

    • c++ compiler

    sudo apt install build-essential

    • geo packages

    sudo apt install proj-bin libgeos-dev libproj-dev

    • depending on the pre-installed packages it could be required to install further packages

    sudo apt install graphviz libgeos++-dev

    • python develop libraries

    sudo apt install python3.6-dev

    macOS & windows

    The installation on macOS is not tested yet. The following commands are possibly needed:

    brew install geos

    sudo port install graphviz

    The installation on Windows is not tested yet.

    How to start with MLAir

    In this section, we show three examples how to work with MLAir. Note, that for these examples MLAir was installed using the distribution file. In case you are using the git clone it is required to adjust the import path if not directly executed inside the source directory of MLAir. There is also a downloadable Jupyter Notebook provided in that you can run the following examples. Note that this notebook still requires an installation of MLAir.

    Example 1

    We start MLAir in a dry run without any modification. Just import mlair and run it.

    import mlair
    
    # just give it a dry run without any modification 
    mlair.run()

    The logging output will show you many informations. Additional information (including debug messages) are collected inside the experiment path in the logging folder.

    INFO: DefaultWorkflow started
    INFO: ExperimentSetup started
    INFO: Experiment path is: /home/<usr>/mlair/testrun_network 
    ...
    INFO: load data for DEBW107 from JOIN
    INFO: load data for DEBY081 from JOIN
    INFO: load data for DEBW013 from JOIN
    INFO: load data for DEBW076 from JOIN
    INFO: load data for DEBW087 from JOIN
    ...
    INFO: Training started
    ...
    INFO: DefaultWorkflow finished after 0:03:04 (hh:mm:ss)

    Example 2

    Now we update the stations and customise the window history size parameter.

    import mlair
    
    # our new stations to use
    stations = ['DEBW030', 'DEBW037', 'DEBW031', 'DEBW015', 'DEBW107']
    
    # expanded temporal context to 14 (days, because of default sampling="daily")
    window_history_size = 14
    
    # restart the experiment with little customisation
    mlair.run(stations=stations, 
              window_history_size=window_history_size)

    The output looks similar, but we can see, that the new stations are loaded.

    INFO: DefaultWorkflow started
    INFO: ExperimentSetup started
    ...
    INFO: load data for DEBW030 from JOIN 
    INFO: load data for DEBW037 from JOIN 
    INFO: load data for DEBW031 from JOIN 
    INFO: load data for DEBW015 from JOIN 
    ...
    INFO: Training started
    ...
    INFO: DefaultWorkflow finished after 00:02:03 (hh:mm:ss)

    Example 3

    Let's just apply our trained model to new data. Therefore we keep the window history size parameter but change the stations. In the run method, we need to disable the trainable and create new model parameters. MLAir will use the model we have trained before. Note, this only works if the experiment path has not changed or a suitable trained model is placed inside the experiment path.

    import mlair
    
    # our new stations to use
    stations = ['DEBY002', 'DEBY079']
    
    # same setting for window_history_size
    window_history_size = 14
    
    # run experiment without training
    mlair.run(stations=stations, 
              window_history_size=window_history_size, 
              create_new_model=False, 
              train_model=False)

    We can see from the terminal that no training was performed. Analysis is now made on the new stations.

    INFO: DefaultWorkflow started
    ...
    INFO: No training has started, because train_model parameter was false. 
    ...
    INFO: DefaultWorkflow finished after 0:01:27 (hh:mm:ss)

    Default Workflow

    MLAir is constituted of so-called run_modules which are executed in a distinct order called workflow. MLAir provides a default_workflow. This workflow runs the run modules ExperimentSetup, PreProcessing, ModelSetup, Training, and PostProcessing one by one.

    Sketch of the default workflow.

    import mlair
    
    # create your custom MLAir workflow
    DefaultWorkflow = mlair.DefaultWorkflow()
    # execute default workflow
    DefaultWorkflow.run()

    The output of running this default workflow will be structured like the following.

    INFO: DefaultWorkflow started
    INFO: ExperimentSetup started
    ...
    INFO: ExperimentSetup finished after 00:00:01 (hh:mm:ss)
    INFO: PreProcessing started
    ...
    INFO: PreProcessing finished after 00:00:11 (hh:mm:ss)
    INFO: ModelSetup started
    ...
    INFO: ModelSetup finished after 00:00:01 (hh:mm:ss)
    INFO: Training started
    ...
    INFO: Training finished after 00:02:15 (hh:mm:ss)
    INFO: PostProcessing started
    ...
    INFO: PostProcessing finished after 00:01:37 (hh:mm:ss)
    INFO: DefaultWorkflow finished after 00:04:05 (hh:mm:ss)

    Customised Run Module and Workflow

    It is possible to create new custom run modules. A custom run module is required to inherit from the base class RunEnvironment and to hold the constructor method __init__(). This method has to execute the module on call. In the following example, this is done by using the _run() method that is called by the initialiser. It is possible to parse arguments to the custom run module as shown.

    import mlair
    import logging
    
    class CustomStage(mlair.RunEnvironment):
        """A custom MLAir stage for demonstration."""
    
        def __init__(self, test_string):
            super().__init__()  # always call super init method
            self._run(test_string)  # call a class method
    
        def _run(self, test_string):
            logging.info("Just running a custom stage.")
            logging.info("test_string = " + test_string)
            epochs = self.data_store.get("epochs")
            logging.info("epochs = " + str(epochs))

    If a custom run module is defined, it is required to adjust the workflow. For this, you need to load the empty Workflow class and add each run module that is required. The order of adding modules defines the order of execution if running the workflow.

    # create your custom MLAir workflow
    CustomWorkflow = mlair.Workflow()
    # provide stages without initialisation
    CustomWorkflow.add(mlair.ExperimentSetup, epochs=128)
    # add also keyword arguments for a specific stage
    CustomWorkflow.add(CustomStage, test_string="Hello World")
    # finally execute custom workflow in order of adding
    CustomWorkflow.run()

    The output will look like:

    INFO: Workflow started
    ...
    INFO: ExperimentSetup finished after 00:00:12 (hh:mm:ss)
    INFO: CustomStage started
    INFO: Just running a custom stage.
    INFO: test_string = Hello World
    INFO: epochs = 128
    INFO: CustomStage finished after 00:00:01 (hh:mm:ss)
    INFO: Workflow finished after 00:00:13 (hh:mm:ss)

    Custom Model

    Create your own model to run your personal experiment. To guarantee a proper integration in the MLAir workflow, models are restricted to inherit from the AbstractModelClass. This will ensure a smooth training and evaluation behaviour.

    How to create a customised model?

    • Create a new model class inheriting from AbstractModelClass
    from mlair import AbstractModelClass
    
    class MyCustomisedModel(AbstractModelClass):
    
        def __init__(self, input_shape: list, output_shape: list):
    
            # set attributes shape_inputs and shape_outputs
            super().__init__(input_shape[0], output_shape[0])
    
            # apply to model
            self.set_model()
            self.set_compile_options()
            self.set_custom_objects(loss=self.compile_options['loss'])
    • Make sure to add the super().__init__() and at least set_model() and set_compile_options() to your custom init method.
    • The shown model expects a single input and output branch provided in a list. Therefore shapes of input and output are extracted and then provided to the super class initialiser.
    • Some general settings like the dropout rate are set in the init method additionally.
    • If you have custom objects in your model, that are not part of the keras or tensorflow frameworks, you need to add them to custom objects. To do this, call set_custom_objects with arbitrarily kwargs. In the shown example, the loss has been added for demonstration only, because we use a build-in loss function. Nonetheless, we always encourage you to add the loss as custom object, to prevent potential errors when loading an already created model instead of training a new one.
    • Now build your model inside set_model() by using the instance attributes self._input_shape and self._output_shape and storing the model as self.model.
    import keras
    from keras.layers import PReLU, Input, Conv2D, Flatten, Dropout, Dense
    
    class MyCustomisedModel(AbstractModelClass):
    
        def set_model(self):
            x_input = Input(shape=self._input_shape)
            x_in = Conv2D(4, (1, 1))(x_input)
            x_in = PReLU()(x_in)
            x_in = Flatten()(x_in)
            x_in = Dropout(0.1)(x_in)
            x_in = Dense(16)(x_in)
            x_in = PReLU()(x_in)
            x_in = Dense(self._output_shape)(x_in)
            out = PReLU()(x_in)
            self.model = keras.Model(inputs=x_input, outputs=[out])
    • Your are free how to design your model. Just make sure to save it in the class attribute model.
    • Additionally, set your custom compile options including the loss definition.
    from keras.losses import mean_squared_error as mse
    
    class MyCustomisedModel(AbstractModelClass):
    
        def set_compile_options(self):
            self.initial_lr = 1e-2
            self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
            self.loss = mse
            self.compile_options = {"metrics": ["mse", "mae"]}
    • The allocation of the instance parameters initial_lr, optimizer, and lr_decay could be also part of the model class' initialiser. The same applies to self.loss and compile_options, but we recommend to use the set_compile_options method for the definition of parameters, that are related to the compile options.

    • More important is that the compile options are actually saved. There are three ways to achieve this.

      • (1): Set all compile options by parsing a dictionary with all options to self.compile_options.

      • (2): Set all compile options as instance attributes. MLAir will search for these attributes and store them.

      • (3): Define your compile options partly as dictionary and instance attributes (as shown in this example).

      • If using (3) and defining the same compile option with different values, MLAir will raise an error.

        Incorrect: (Will raise an error because of a mismatch for the optimizer parameter.)

        def set_compile_options(self):
            self.optimizer = keras.optimizers.SGD()
            self.loss = keras.losses.mean_squared_error
            self.compile_options = {"optimizer" = keras.optimizers.Adam()}

    How to plug in the customised model into the workflow?

    • Make use of the model argument and pass MyCustomisedModel when instantiating a workflow.
    from mlair.workflows import DefaultWorkflow
    
    workflow = DefaultWorkflow(model=MyCustomisedModel)
    workflow.run()

    Specials for Branched Models

    • If you have a branched model with multiple outputs, you need either set only a single loss for all branch outputs or provide the same number of loss functions considering the right order.
    class MyCustomisedModel(AbstractModelClass):
    
        def set_model(self):
            ...
            self.model = keras.Model(inputs=x_input, outputs=[out_minor_1, out_minor_2, out_main])
    
        def set_compile_options(self):
            self.loss = [keras.losses.mean_absolute_error] +  # for out_minor_1
                        [keras.losses.mean_squared_error] +   # for out_minor_2
                        [keras.losses.mean_squared_error]     # for out_main

    How to access my customised model?

    If the customised model is created, you can easily access the model with

    >>> MyCustomisedModel().model
    <your custom model>

    The loss is accessible via

    >>> MyCustomisedModel().loss
    <your custom loss>

    You can treat the instance of your model as instance but also as the model itself. If you call a method, that refers to the model instead of the model instance, you can directly apply the command on the instance instead of adding the model parameter call.

    >>> MyCustomisedModel().model.compile(**kwargs) == MyCustomisedModel().compile(**kwargs)
    True

    Data Handlers

    Data handlers are responsible for all tasks related to data like data acquisition, preparation and provision. A data handler must inherit from the abstract base class AbstractDataHandler and requires the implementation of the __init__() method and the accessors get_X() and get_Y(). In the following, we show an example how a custom data handler could look like.

    import datetime as dt
    import numpy as np
    import pandas as pd
    import xarray as xr
    
    from mlair.data_handler import AbstractDataHandler
    
    class DummyDataHandler(AbstractDataHandler):
    
        def __init__(self, name, number_of_samples=None):
            """This data handler takes a name argument and the number of samples to generate. If not provided, a random 
            number between 100 and 150 is set."""
            super().__init__()
            self.name = name
            self.number_of_samples = number_of_samples if number_of_samples is not None else np.random.randint(100, 150)
            self._X = self.create_X()
            self._Y = self.create_Y()
    
        def create_X(self):
            """Inputs are random numbers between 0 and 10 with shape (no_samples, window=14, variables=5)."""
            X = np.random.randint(0, 10, size=(self.number_of_samples, 14, 5))  # samples, window, variables
            datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
            return xr.DataArray(X, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
                                                                                     "window": range(14),
                                                                                     "variables": range(5)})
        
        def create_Y(self):
            """Targets are normal distributed random numbers with shape (no_samples, window=5, variables=1)."""
            Y = np.round(0.5 * np.random.randn(self.number_of_samples, 5, 1), 1)  # samples, window, variables
            datelist = pd.date_range(dt.datetime.today().date(), periods=self.number_of_samples, freq="H").tolist()
            return xr.DataArray(Y, dims=['datetime', 'window', 'variables'], coords={"datetime": datelist,
                                                                                     "window": range(5),
                                                                                     "variables": range(1)})
    
        def get_X(self, upsampling=False, as_numpy=False):
            """Upsampling parameter is not used for X."""
            return np.copy(self._X) if as_numpy is True else self._X
    
        def get_Y(self, upsampling=False, as_numpy=False):
            """Upsampling parameter is not used for Y."""
            return np.copy(self._Y) if as_numpy is True else self._Y
    
        def __str__(self):
            return self.name
    

    Special Remarks

    Workaround to skip geographical plot

    If it is not possible to install all required geo libraries on your system, a good compromise is to skip the creation of the geographical plot. Therefore, it is required to remove the plot from the plot_list manually. We recommend to use this code snippet as a starting point.

    from mlair.helpers import remove_items
    from mlair.configuration.defaults import DEFAULT_PLOT_LIST
    
    mlair.run(plot_list=remove_items(DEFAULT_PLOT_LIST, "PlotStationMap"))
    

    Special instructions for installation on Jülich HPC systems

    Please note, that the HPC setup is customised for JUWELS and HDFML. When using another HPC system, you can use the HPC setup files as a skeleton and customise it to your needs.

    The following instruction guide you through the installation on JUWELS and HDFML.

    • Clone the repo to HPC system (we recommend to place it in /p/projects/<project name>).
    • Setup venv by executing source setupHPC.sh. This script loads all pre-installed modules and creates a venv for all other packages. Furthermore, it creates slurm/batch scripts to execute code on compute nodes. You have to enter the HPC project's budget name (--account flag).
    • The default external data path on JUWELS and HDFML is set to /p/project/deepacf/intelliaq/<user>/DATA/toar_<sampling>. To choose a different location open run.py and add the following keyword argument to ExperimentSetup: data_path=<your>/<custom>/<path>.
    • Execute python run.py on a login node to download example data. The program will throw an OSerror after downloading.
    • Execute either sbatch run_juwels_develgpus.bash or sbatch run_hdfml_batch.bash to verify that the setup went well.
    • Currently cartopy is not working on our HPC system, therefore PlotStations does not create any output.

    Note: The method PartitionCheck currently only checks if the hostname starts with ju or hdfmll. Therefore, it might be necessary to adopt the if statement in PartitionCheck._run.

    Security using JOIN

    • To use hourly data from ToarDB via JOIN interface, a private token is required. Request your personal access token and add it to src/join_settings.py in the hourly data section. Replace the TOAR_SERVICE_URL and the Authorization value. To make sure, that this sensitive data is not uploaded to the remote server, use the following command to prevent git from tracking this file: git update-index --assume-unchanged src/join_settings.py