.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/00-released_examples/02-DOE_ML.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_00-released_examples_02-DOE_ML.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_00-released_examples_02-DOE_ML.py:


.. _doe_ml:

Design of Experiments and Machine Learning model building
---------------------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 9-24

Objective
=====================================================================================

Water enters a Mixing Elbow from two Inlets; Hot (313 K) and Cold (293 K) and exits
from Outlet. Using PyFluent in the background, this example runs Design of Experiments
with Cold Inlet Velocity and Hot Inlet Velocity as Input Parameters and Outlet
Temperature as an Output Parameter.

Results can be visualized using a Response Surface. Finally, Supervised Machine
Learning Regression Task is performed to build the ML Model.

This example demonstrates:

* Design of Experiment, Fluent setup and simulation using PyFluent.
* Building of Supervised Machine Learning Model.

.. GENERATED FROM PYTHON SOURCE LINES 24-27

.. code-block:: default


    # sphinx_gallery_thumbnail_path = '_static/doe_ml_predictions_regression.png'


.. GENERATED FROM PYTHON SOURCE LINES 28-30

Import required libraries/modules
=================================

.. GENERATED FROM PYTHON SOURCE LINES 30-44

.. code-block:: default


    from pathlib import Path

    import ansys.fluent.core as pyfluent
    from ansys.fluent.core import examples
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import plotly.express as px
    import plotly.graph_objects as go
    import seaborn as sns
    import tensorflow as tf
    from tensorflow import keras


.. GENERATED FROM PYTHON SOURCE LINES 45-50

Specifying save path
====================
* save_path can be specified as Path("E:/", "pyfluent-examples-tests") or
* Path("E:/pyfluent-examples-tests") in a Windows machine for example,  or
* Path("~/pyfluent-examples-tests") in Linux.

.. GENERATED FROM PYTHON SOURCE LINES 50-59

.. code-block:: default


    save_path = Path(pyfluent.EXAMPLES_PATH)

    import_filename = examples.download_file(
        "elbow.cas.h5",
        "pyfluent/examples/DOE-ML-Mixing-Elbow",
        save_path=save_path,
    )


.. GENERATED FROM PYTHON SOURCE LINES 60-62

Fluent Solution Setup
=====================

.. GENERATED FROM PYTHON SOURCE LINES 64-66

Launch Fluent session with solver mode
======================================

.. GENERATED FROM PYTHON SOURCE LINES 66-78

.. code-block:: default


    solver = pyfluent.launch_fluent(
        product_version="23.1.0",
        mode="solver",
        show_gui=False,
        version="3d",
        precision="double",
        processor_count=2,
    )
    solver.health_check_service.check_health()



.. GENERATED FROM PYTHON SOURCE LINES 79-81

Read case
=========

.. GENERATED FROM PYTHON SOURCE LINES 81-84

.. code-block:: default


    solver.tui.file.read_case(import_filename)


.. GENERATED FROM PYTHON SOURCE LINES 85-90

Design of Experiments
=====================
* Define Manual DOE as numpy arrays
* Run cases in sequence
* Populate results (Mass Weighted Average of Temperature at Outlet) in resArr

.. GENERATED FROM PYTHON SOURCE LINES 90-120

.. code-block:: default


    coldVelArr = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7])
    hotVelArr = np.array([0.8, 1, 1.2, 1.4, 1.6, 1.8, 2.0])
    resArr = np.zeros((coldVelArr.shape[0], hotVelArr.shape[0]))

    for idx1, coldVel in np.ndenumerate(coldVelArr):
        for idx2, hotVel in np.ndenumerate(hotVelArr):
            solver.setup.boundary_conditions.velocity_inlet["cold-inlet"].vmag = {
                "option": "value",
                "value": coldVel,
            }

            solver.setup.boundary_conditions.velocity_inlet["hot-inlet"].vmag = {
                "option": "value",
                "value": hotVel,
            }

            solver.tui.solve.initialize.initialize_flow("yes")
            solver.tui.solve.iterate(200)

            res_tui = solver.scheme_eval.exec(
                (
                    "(ti-menu-load-string "
                    '"/report/surface-integrals/mass-weighted-avg outlet () '
                    'temperature no")',
                )
            )
            resArr[idx1][idx2] = eval(res_tui.split(" ")[-1])



.. GENERATED FROM PYTHON SOURCE LINES 121-123

Close the session
=================

.. GENERATED FROM PYTHON SOURCE LINES 123-126

.. code-block:: default


    solver.exit()


.. GENERATED FROM PYTHON SOURCE LINES 127-129

Plot Response Surface using Plotly
==================================

.. GENERATED FROM PYTHON SOURCE LINES 129-155

.. code-block:: default


    fig = go.Figure(data=[go.Surface(z=resArr.T, x=coldVelArr, y=hotVelArr)])

    fig.update_layout(
        title={
            "text": "Mixing Elbow Response Surface",
            "y": 0.9,
            "x": 0.5,
            "xanchor": "center",
            "yanchor": "top",
        }
    )

    fig.update_layout(
        scene=dict(
            xaxis_title="Cold Inlet Vel (m/s)",
            yaxis_title="Hot Inlet Vel (m/s)",
            zaxis_title="Outlet Temperature (K)",
        ),
        width=600,
        height=600,
        margin=dict(l=80, r=80, b=80, t=80),
    )
    fig.show()



.. GENERATED FROM PYTHON SOURCE LINES 156-158

Supervised ML for a Regression Task
===================================

.. GENERATED FROM PYTHON SOURCE LINES 160-162

Create Pandas Dataframe for ML Model Input
==========================================

.. GENERATED FROM PYTHON SOURCE LINES 162-181

.. code-block:: default

    coldVelList = []
    hotVelList = []
    ResultList = []

    for idx1, coldVel in np.ndenumerate(coldVelArr):
        for idx2, hotVel in np.ndenumerate(hotVelArr):
            coldVelList.append(coldVel)
            hotVelList.append(hotVel)
            ResultList.append(resArr[idx1][idx2])

    tempDict = {"coldVel": coldVelList, "hotVel": hotVelList, "Result": ResultList}

    df = pd.DataFrame.from_dict(tempDict)

    from sklearn.compose import ColumnTransformer
    from sklearn.model_selection import train_test_split
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import PolynomialFeatures, StandardScaler


.. GENERATED FROM PYTHON SOURCE LINES 182-187

Using scikit-learn
==================
* Prepare Features (X) and Label (y) using a Pre-Processing Pipeline
* Train-Test (80-20) Split
* Add Polynomial Features to improve ML Model

.. GENERATED FROM PYTHON SOURCE LINES 187-216

.. code-block:: default


    poly_features = PolynomialFeatures(degree=2, include_bias=False)

    transformer1 = Pipeline(
        [
            ("poly_features", poly_features),
            ("std_scaler", StandardScaler()),
        ]
    )


    x_ct = ColumnTransformer(
        [
            ("transformer1", transformer1, ["coldVel", "hotVel"]),
        ],
        remainder="drop",
    )

    train_set, test_set = train_test_split(df, test_size=0.2, random_state=42)

    X_train = x_ct.fit_transform(train_set)
    X_test = x_ct.fit_transform(test_set)

    y_train = train_set["Result"]
    y_test = test_set["Result"]
    y_train = np.ravel(y_train.T)
    y_test = np.ravel(y_test.T)



.. GENERATED FROM PYTHON SOURCE LINES 217-222

* Define functions for:
* Cross-Validation and Display Scores (scikit-learn)
* Training the Model (scikit-learn)
* Prediction on Unseen/Test Data (scikit-learn)
* Parity Plot (Matplotlib and Seaborn)

.. GENERATED FROM PYTHON SOURCE LINES 222-299

.. code-block:: default


    from pprint import pprint  # noqa: F401

    from sklearn.ensemble import RandomForestRegressor  # noqa: F401
    from sklearn.linear_model import LinearRegression  # noqa: F401
    from sklearn.metrics import r2_score
    from sklearn.model_selection import RepeatedKFold, cross_val_score
    from xgboost import XGBRegressor

    np.set_printoptions(precision=2)


    def display_scores(scores):
        print("\nCross-Validation Scores:", scores)
        print("Mean:%0.2f" % (scores.mean()))
        print("Std. Dev.:%0.2f" % (scores.std()))


    def fit_and_predict(model):
        cv = RepeatedKFold(n_splits=5, n_repeats=3, random_state=42)
        cv_scores = cross_val_score(
            model, X_train, y_train, scoring="neg_mean_squared_error", cv=cv
        )
        rmse_scores = np.sqrt(-cv_scores)
        display_scores(rmse_scores)

        model.fit(X_train, y_train)
        train_predictions = model.predict(X_train)
        test_predictions = model.predict(X_test)
        print(train_predictions.shape[0])
        print("\n\nCoefficient Of Determination")
        print("Train Data R2 Score: %0.3f" % (r2_score(train_predictions, y_train)))
        print("Test Data R2 Score: %0.3f" % (r2_score(test_predictions, y_test)))
        print(
            "\n\nPredictions - Ground Truth (Kelvin): ", (test_predictions - y_test), "\n"
        )
        #    print("\n\nModel Parameters:")
        #    pprint(model.get_params())

        com_train_set = train_set
        com_test_set = test_set

        train_list = []
        for i in range(train_predictions.shape[0]):
            train_list.append("Train")

        test_list = []
        for i in range(test_predictions.shape[0]):
            test_list.append("Test")

        com_train_set["Result"] = train_predictions.tolist()
        com_train_set["Set"] = train_list
        com_test_set["Result"] = test_predictions.tolist()
        com_test_set["Set"] = test_list

        df_combined = pd.concat([com_train_set, com_test_set])

        df_combined.to_csv("PyFluent_Output.csv", header=True, index=False)

        fig = plt.figure(figsize=(12, 5))

        fig.add_subplot(121)
        sns.regplot(x=y_train, y=train_predictions, color="g")
        plt.title("Train Data", fontsize=16)
        plt.xlabel("Ground Truth", fontsize=12)
        plt.ylabel("Predictions", fontsize=12)

        fig.add_subplot(122)
        sns.regplot(x=y_test, y=test_predictions, color="g")
        plt.title("Unseen Data", fontsize=16)
        plt.xlabel("Ground Truth", fontsize=12)
        plt.ylabel("Predictions", fontsize=12)

        plt.tight_layout()
        plt.show()



.. GENERATED FROM PYTHON SOURCE LINES 300-303

Select the Model from Linear, Random Forest or XGBoost
======================================================
* Call fit_and_predict

.. GENERATED FROM PYTHON SOURCE LINES 303-312

.. code-block:: default


    # model = LinearRegression()
    model = XGBRegressor(
        n_estimators=100, max_depth=10, eta=0.3, subsample=0.8, random_state=42
    )
    # model = RandomForestRegressor(random_state=42)

    fit_and_predict(model)


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Show graph
==========

.. GENERATED FROM PYTHON SOURCE LINES 315-318

.. code-block:: default


    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 319-322

.. image:: ../../_static/doe_ml_predictions_regression.png
   :align: center
   :alt: Regression Model Predictions

.. GENERATED FROM PYTHON SOURCE LINES 324-325

Regression Model Predictions

.. GENERATED FROM PYTHON SOURCE LINES 327-329

3D Visualization of Model Predictions on Train & Test Set
=========================================================

.. GENERATED FROM PYTHON SOURCE LINES 329-361

.. code-block:: default


    df = pd.read_csv("PyFluent_Output.csv")

    fig = px.scatter_3d(df, x="coldVel", y="hotVel", z="Result", color="Set")
    fig.update_traces(marker=dict(size=4))
    fig.update_layout(legend=dict(yanchor="top", y=1, xanchor="left", x=0.0))

    fig.add_traces(go.Surface(z=resArr.T, x=coldVelArr, y=hotVelArr))

    fig.update_layout(
        title={
            "text": "Mixing Elbow Response Surface",
            "y": 0.9,
            "x": 0.5,
            "xanchor": "center",
            "yanchor": "top",
        }
    )

    fig.update_layout(
        scene=dict(
            xaxis_title="Cold Inlet Vel (m/s)",
            yaxis_title="Hot Inlet Vel (m/s)",
            zaxis_title="Outlet Temperature (K)",
        ),
        width=500,
        height=500,
        margin=dict(l=80, r=80, b=80, t=80),
    )

    fig.show()


.. GENERATED FROM PYTHON SOURCE LINES 362-364

TensorFlow and Keras Neural Network Regression
==============================================

.. GENERATED FROM PYTHON SOURCE LINES 364-443

.. code-block:: default


    print("TensorFlow version is:", tf.__version__)
    keras.backend.clear_session()
    np.random.seed(42)
    tf.random.set_seed(42)

    model = keras.models.Sequential(
        [
            keras.layers.Dense(
                20,
                activation="relu",
                input_shape=X_train.shape[1:],
                kernel_initializer="lecun_normal",
            ),
            keras.layers.BatchNormalization(),
            keras.layers.Dense(20, activation="relu", kernel_initializer="lecun_normal"),
            keras.layers.BatchNormalization(),
            keras.layers.Dense(20, activation="relu", kernel_initializer="lecun_normal"),
            keras.layers.BatchNormalization(),
            keras.layers.Dense(1),
        ]
    )

    optimizer = tf.keras.optimizers.Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999)

    model.compile(loss="mean_squared_error", optimizer=optimizer)
    checkpoint_cb = keras.callbacks.ModelCheckpoint(
        "my_keras_model.h5", save_best_only=True
    )
    early_stopping_cb = keras.callbacks.EarlyStopping(
        patience=30, restore_best_weights=True
    )

    model.summary()

    # keras.utils.plot_model(model, show_shapes=True,) # to_file='dot_img.png', )

    history = model.fit(
        X_train,
        y_train,
        epochs=250,
        validation_split=0.2,
        callbacks=[checkpoint_cb, early_stopping_cb],
    )
    model = keras.models.load_model("my_keras_model.h5")

    print(history.params)

    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    plt.show()

    train_predictions = model.predict(X_train)
    test_predictions = model.predict(X_test)
    train_predictions = np.ravel(train_predictions.T)
    test_predictions = np.ravel(test_predictions.T)
    print(test_predictions.shape)

    print("\n\nTrain R2: %0.3f" % (r2_score(train_predictions, y_train)))
    print("Test R2: %0.3f" % (r2_score(test_predictions, y_test)))
    print("Predictions - Ground Truth (Kelvin): ", (test_predictions - y_test))

    fig = plt.figure(figsize=(12, 5))

    fig.add_subplot(121)
    sns.regplot(x=y_train, y=train_predictions, color="g")
    plt.title("Train Data", fontsize=16)
    plt.xlabel("Ground Truth", fontsize=12)
    plt.ylabel("Predictions", fontsize=12)

    fig.add_subplot(122)
    sns.regplot(x=y_test, y=test_predictions, color="g")
    plt.title("Test/Unseen Data", fontsize=16)
    plt.xlabel("Ground Truth", fontsize=12)
    plt.ylabel("Predictions", fontsize=12)

    plt.tight_layout()



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Show graph
==========

.. GENERATED FROM PYTHON SOURCE LINES 446-449

.. code-block:: default


    plt.show()


.. GENERATED FROM PYTHON SOURCE LINES 450-453

.. image:: ../../_static/doe_ml_validation_loss.png
   :align: center
   :alt: Neural Network Validation Loss

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Neural Network Validation Loss

.. GENERATED FROM PYTHON SOURCE LINES 458-461

.. image:: ../../_static/doe_ml_predictions_neural_network.png
   :align: center
   :alt: Neural Network Predictions

.. GENERATED FROM PYTHON SOURCE LINES 463-464

Neural Network Predictions


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.000 seconds)


.. _sphx_glr_download_examples_00-released_examples_02-DOE_ML.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 02-DOE_ML.py <02-DOE_ML.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 02-DOE_ML.ipynb <02-DOE_ML.ipynb>`


.. only:: html

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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_