.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/00-fluent/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-fluent_DOE_ML.py>`
        to download the full example code.

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

.. _sphx_glr_examples_00-fluent_DOE_ML.py:

.. _doe_ml:

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

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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.

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

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.. code-block:: Python


    from pathlib import Path

    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

    import ansys.fluent.core as pyfluent
    from ansys.fluent.core import examples


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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 48-57

.. code-block:: Python


    save_path = Path(pyfluent.EXAMPLES_PATH)

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


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

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

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.. code-block:: Python


    solver = pyfluent.launch_fluent(
        precision="double",
        processor_count=2,
        version="3d",
    )
    solver.health_check.status()



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Read case
=========

.. GENERATED FROM PYTHON SOURCE LINES 76-79

.. code-block:: Python


    solver.settings.file.read_case(file_name=import_filename)


.. GENERATED FROM PYTHON SOURCE LINES 80-85

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 85-116

.. code-block:: Python


    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):
            cold_inlet = solver.settings.setup.boundary_conditions.velocity_inlet[
                "cold-inlet"
            ]
            cold_inlet.momentum.velocity.value = coldVel

            hot_inlet = solver.settings.setup.boundary_conditions.velocity_inlet[
                "hot-inlet"
            ]
            hot_inlet.momentum.velocity.value = hotVel

            solver.settings.solution.initialization.initialization_type = "standard"
            solver.settings.solution.initialization.standard_initialize()
            solver.settings.solution.run_calculation.iterate(iter_count=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])



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

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.. code-block:: Python


    solver.exit()


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

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.. code-block:: Python


    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()



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

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

.. GENERATED FROM PYTHON SOURCE LINES 158-177

.. code-block:: Python

    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 178-183

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 183-212

.. code-block:: Python


    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 213-218

* 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 218-297

.. code-block:: Python


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


    def fit_and_predict(model):
        """Fit abd predict."""
        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()



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

.. GENERATED FROM PYTHON SOURCE LINES 301-310

.. code-block:: Python


    # 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
==========

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.. code-block:: Python


    plt.show()


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

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Regression Model Predictions

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

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.. code-block:: Python


    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()


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

.. GENERATED FROM PYTHON SOURCE LINES 362-441

.. code-block:: Python


    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
==========

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.. code-block:: Python


    plt.show()


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

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Neural Network Validation Loss

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

.. GENERATED FROM PYTHON SOURCE LINES 461-462

Neural Network Predictions


.. _sphx_glr_download_examples_00-fluent_DOE_ML.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: DOE_ML.zip <DOE_ML.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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