{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## General Information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is used to train a simple neural network model to predict the chemistry in the barite benchmark (50x50 grid). The training data is stored in the repository using **git large file storage** and can be downloaded after the installation of git lfs using the `git lfs pull` command.\n", "\n", "It is then recommended to create a Python environment using miniconda. The necessary dependencies are contained in `environment.yml` and can be installed using `conda env create -f environment.yml`.\n", "\n", "The data set is divided into a design and result part and consists of the iterations of a reference simulation. The design part of the data set contains the chemical concentrations at time $t$ and the result part at time $t+1$, which are to be learned by the model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running Keras in version 3.8.0\n" ] } ], "source": [ "import keras\n", "import h5py\n", "import numpy as np\n", "import pandas as pd\n", "import time\n", "import sklearn.model_selection as sk\n", "import matplotlib.pyplot as plt\n", "from sklearn.cluster import KMeans\n", "from sklearn.pipeline import Pipeline, make_pipeline\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from imblearn.over_sampling import SMOTE\n", "from imblearn.under_sampling import RandomUnderSampler\n", "from imblearn.over_sampling import RandomOverSampler\n", "from collections import Counter\n", "import os\n", "from preprocessing import *\n", "from sklearn import set_config\n", "from importlib import reload\n", "set_config(transform_output = \"pandas\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define parameters" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dtype = \"float32\"\n", "activation = \"relu\"\n", "\n", "lr = 0.001\n", "batch_size = 512\n", "epochs = 50 # default 400 epochs\n", "\n", "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n", " initial_learning_rate=lr,\n", " decay_steps=2000,\n", " decay_rate=0.9,\n", " staircase=True\n", ")\n", "\n", "optimizer_simple = keras.optimizers.Adam(learning_rate=lr_schedule)\n", "optimizer_large = keras.optimizers.Adam(learning_rate=lr_schedule)\n", "\n", "loss = keras.losses.MeanSquaredError()\n", "\n", "sample_fraction = 0.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup the model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model: \"sequential\"\n",
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       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense (Dense)                   │ (None, 128)            │         1,664 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (Dense)                 │ (None, 12)             │         1,548 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Total params: 19,724 (77.05 KB)\n",
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\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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Model: \"sequential_1\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_3 (Dense)                 │ (None, 512)            │         6,656 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_6 (Dense)                 │ (None, 12)             │         6,156 │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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 Total params: 1,062,924 (4.05 MB)\n",
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 Trainable params: 1,062,924 (4.05 MB)\n",
       "
\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# large model\n", "model_large = keras.Sequential(\n", " [keras.layers.Input(shape=(12,), dtype=dtype),\n", " keras.layers.Dense(512, activation='relu', dtype=dtype),\n", " keras.layers.Dense(1024, activation='relu', dtype=dtype),\n", " keras.layers.Dense(512, activation='relu', dtype=dtype),\n", " keras.layers.Dense(12, dtype=dtype)\n", " ])\n", "\n", "model_large.compile(optimizer=optimizer_large, loss = loss)\n", "model_large.summary()\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# model from paper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define transformer functions" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def Safelog(val):\n", " # get range of vector\n", " if val > 0:\n", " return np.log10(val)\n", " elif val < 0:\n", " return -np.log10(-val)\n", " else:\n", " return 0\n", "\n", "def Safeexp(val):\n", " if val > 0:\n", " return -10 ** -val\n", " elif val < 0:\n", " return 10 ** val\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# ? Why does the charge is using another logarithm than the other species\n", "\n", "func_dict_in = {\n", " \"H\" : np.log1p,\n", " \"O\" : np.log1p,\n", " \"Charge\" : Safelog,\n", " \"H_0_\" : np.log1p,\n", " \"O_0_\" : np.log1p,\n", " \"Ba\" : np.log1p,\n", " \"Cl\" : np.log1p,\n", " \"S_2_\" : np.log1p,\n", " \"S_6_\" : np.log1p,\n", " \"Sr\" : np.log1p,\n", " \"Barite\" : np.log1p,\n", " \"Celestite\" : np.log1p,\n", "}\n", "\n", "func_dict_out = {\n", " \"H\" : np.expm1,\n", " \"O\" : np.expm1,\n", " \"Charge\" : Safeexp,\n", " \"H_0_\" : np.expm1,\n", " \"O_0_\" : np.expm1,\n", " \"Ba\" : np.expm1,\n", " \"Cl\" : np.expm1,\n", " \"S_2_\" : np.expm1,\n", " \"S_6_\" : np.expm1,\n", " \"Sr\" : np.expm1,\n", " \"Barite\" : np.expm1,\n", " \"Celestite\" : np.expm1,\n", "}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data from `.h5` file and convert it to a `pandas.DataFrame`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n", "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n", "\n", "design = data_file[\"design\"]\n", "results = data_file[\"result\"]\n", "\n", "df_design = pd.DataFrame(np.array(design[\"data\"]).transpose(), columns = np.array(design[\"names\"].asstr()))\n", "df_results = pd.DataFrame(np.array(results[\"data\"]).transpose(), columns = np.array(results[\"names\"].asstr()))\n", "\n", "data_file.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preprocess Data\n", "\n", "The data are preprocessed in the following way:\n", "\n", "1. Label data points in the `design` dataset with `reactive` and `non-reactive` labels using kmeans clustering\n", "2. Transform `design` and `results` data set into log-scaled data.\n", "3. Split data into training and test sets.\n", "4. Learn scaler on training data for `design` and `results` together (option `global`) or individual (option `individual`).\n", "5. Transform training and test data.\n", "6. Split training data into training and validation dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/sklearn/base.py:1474: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n", " return fit_method(estimator, *args, **kwargs)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Amount class 0 before: 0.9879169719169719\n", "Amount class 1 before: 0.012083028083028084\n", "Using Oversampling\n", "Amount class 0 after: 0.5\n", "Amount class 1 after: 0.5\n" ] } ], "source": [ "X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y = preprocessing_training(df_design, df_results, func_dict_in, func_dict_out, \"over\", 'global', 0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Custom Loss function" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess):\n", " df_result = postprocess(df_result_log, func_dict_out, data_min_log, data_max_log) \n", " return keras.losses.Huber + np.sum(((df_result['H'] / df_result['O']) - 2)**2)\n", "\n", "def loss_wrapper(data_min_log, data_max_log, func_dict_out, postprocess):\n", " def loss(df_design_log, df_result_log):\n", " return custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess)\n", " return loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the model" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 0.0021 - val_loss: 3.4232e-05\n", "Epoch 2/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.5182e-05 - val_loss: 3.3009e-05\n", "Epoch 3/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.3553e-05 - val_loss: 3.1858e-05\n", "Epoch 4/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2530e-05 - val_loss: 3.1686e-05\n", "Epoch 5/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1540e-05 - val_loss: 3.1268e-05\n", "Epoch 6/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1264e-05 - val_loss: 3.1947e-05\n", "Epoch 7/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1342e-05 - val_loss: 3.1175e-05\n", "Epoch 8/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1101e-05 - val_loss: 3.1003e-05\n", "Epoch 9/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1035e-05 - val_loss: 3.0818e-05\n", "Epoch 10/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.0654e-05 - val_loss: 3.0667e-05\n", "Epoch 11/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0462e-05 - val_loss: 3.0639e-05\n", "Epoch 12/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.0563e-05 - val_loss: 3.0643e-05\n", "Epoch 13/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.1567e-05 - val_loss: 3.0610e-05\n", "Epoch 14/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0625e-05 - val_loss: 3.0598e-05\n", "Epoch 15/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0962e-05 - val_loss: 3.0589e-05\n", "Epoch 16/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1131e-05 - val_loss: 3.0598e-05\n", "Epoch 17/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0939e-05 - val_loss: 3.0580e-05\n", "Epoch 18/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.1100e-05 - val_loss: 3.0580e-05\n", "Epoch 19/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0586e-05 - val_loss: 3.0579e-05\n", "Epoch 20/20\n", "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.0749e-05 - val_loss: 3.0576e-05\n", "Training took 295.5790858268738 seconds\n" ] } ], "source": [ "# measure time\n", "start = time.time()\n", "callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n", "history = model_simple.fit(X_train.iloc[:, X_train.columns != \"Class\"], \n", " y_train.iloc[:, y_train.columns != \"Class\"], \n", " batch_size = batch_size, \n", " epochs = 20, \n", " validation_data = (X_val.iloc[:, X_val.columns != \"Class\"], y_val.iloc[:, y_val.columns != \"Class\"]),\n", " callbacks = [callback])\n", "\n", "end = time.time()\n", "\n", "print(\"Training took {} seconds\".format(end - start))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 595us/step\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "species = \"H\"\n", "iterations = 1000\n", "cell_offset = 1\n", "y_design = []\n", "y_results = []\n", "y_differences = []\n", "\n", "\n", "df_design_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_design))\n", "df_results_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_results))\n", "\n", "for i in range(0,iterations):\n", " idx = i*50*50 + cell_offset -1\n", " y_design.append(df_design_transformed_scaled.iloc[idx, :])\n", " y_results.append(df_results_transformed_scaled.iloc[idx,:])\n", " \n", "y_design = pd.DataFrame(y_design)\n", "y_results = pd.DataFrame(y_results)\n", "# plt.plot(np.arange(0,iterations), y_design[species], label = \"Design\")\n", "plt.plot(np.arange(0,iterations), y_results[species], label = \"Results\")\n", "\n", "prediction = model_simple.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n", "prediction = pd.DataFrame(prediction, columns = y_results.columns)\n", "\n", "y_results_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(y_results), columns=df_results.columns))\n", "prediction_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(prediction), columns=df_results.columns))\n", "\n", "plt.plot(np.arange(0,iterations), prediction[species], label = \"Prediction\")\n", "plt.xlabel('Iteration')\n", "plt.ylabel(species)\n", "plt.title(species+' Concentration over Iterations')\n", "plt.show()\n", "\n", "plt.plot(np.arange(0,iterations), y_results_back[species], label = \"Results\")\n", "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n", "\n", "plt.show()\n", "\n", "timestep = 1000\n", "plt.imshow(np.array(df_results[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1000 rows × 12 columns

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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"], \"o-\", label = \"Training Loss\")\n", "plt.xlabel(\"Epoch\")\n", "# plt.yscale('log')\n", "plt.ylabel(\"Loss (Huber)\")\n", "plt.grid('on')\n", "\n", "plt.savefig(\"loss_all.png\", dpi=300)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(history.history[\"loss\"][1:], \"o-\", label = \"Training Loss\")\n", "plt.xlabel(\"Epoch\")\n", "# plt.yscale('log')\n", "plt.ylabel(\"Loss (Huber)\")\n", "plt.grid('on')\n", "plt.savefig(\"loss_1_to_end.png\", dpi=300)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test the model" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 395us/step - loss: 2.1143e-06\n" ] }, { "data": { "text/plain": [ "1.9805663669103524e-06" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on all test data\n", "model_simple.evaluate(X_test.iloc[:,X_test.columns != \"Class\"], y_test.iloc[:, y_test.columns != \"Class\"])" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m7727/7727\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 369us/step - loss: 1.0423e-06\n" ] }, { "data": { "text/plain": [ "9.472264537180308e-07" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on non-reactive data\n", "model_simple.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0].iloc[:,y_test.columns != \"Class\"])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 542us/step - loss: 8.8564e-05\n" ] }, { "data": { "text/plain": [ "8.700142643647268e-05" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# test on reactive data\n", "model_simple.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1].iloc[:, y_test.columns != \"Class\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save the model" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "# Save the model\n", "model.save(\"Barite_50_Model_additional_species.keras\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Legacy Code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "def log_scale(df_design, df_result, func_dict):\n", " \n", " df_design = df_design.copy()\n", " df_result = df_result.copy()\n", " \n", " for key in df_design.keys():\n", " if key != \"Class\":\n", " df_design[key] = np.vectorize(func_dict[key])(df_design[key])\n", " df_result[key] = np.vectorize(func_dict[key])(df_result[key])\n", " \n", " return df_design, df_result\n", "\n", "# Get minimum and maximum values for each column\n", "def get_min_max(df_design, df_result):\n", " \n", " min_vals_des = df_design.min()\n", " max_vals_des = df_design.max()\n", " \n", " min_vals_res = df_result.min()\n", " max_vals_res = df_result.max()\n", "\n", " # minimum of input and output data to get global minimum/maximum\n", " data_min = np.minimum(min_vals_des, min_vals_res).to_dict()\n", " data_max = np.maximum(max_vals_des, max_vals_res).to_dict()\n", "\n", " return data_min, data_max\n", "\n", "df_design_log, df_results_log = log_scale(df_design, df_results, func_dict_in)\n", "data_min_log, data_max_log = get_min_max(df_design_log, df_design_log)\n", "\n", "train_min_log, train_max_log = get_min_max(X_train_log, y_train_log)\n", "test_min_log, test_max_log = get_min_max(X_test_log, y_test_log)\n", "\n", "X_train_preprocess = preprocess(X_train_log, func_dict_in, train_min_log, train_max_log)\n", "y_train_preprocess = preprocess(y_train_log, func_dict_in, train_min_log, train_max_log)\n", "\n", "X_test_preprocess = preprocess(X_test_log, func_dict_in, test_min_log, test_max_log)\n", "y_test_preprocess = preprocess(y_test_log, func_dict_in, test_min_log, test_max_log)\n", "\n", "X_train_log, y_train_log = log_scale(X_train, y_train, func_dict_in)\n", "X_test_log, y_test_log = log_scale(X_test, y_test, func_dict_in)\n", "\n", "\n", "def preprocess(data, func_dict, data_min, data_max):\n", " data = data.copy()\n", " for key in data.keys():\n", " if key != \"Class\":\n", " data[key] = (data[key] - data_min[key]) / (data_max[key] - data_min[key])\n", "\n", " return data\n", "\n", "def postprocess(data, func_dict, data_min, data_max):\n", " data = data.copy()\n", " for key in data.keys():\n", " if key != \"Class\":\n", " data[key] = data[key] * (data_max[key] - data_min[key]) + data_min[key]\n", " data[key] = np.vectorize(func_dict[key])(data[key])\n", " return data\n", "\n", "X_train, X_val, y_train, y_val = sk.train_test_split(X_train_preprocess, y_train_preprocess, test_size = 0.1)\n", "\n", "pp_design = preprocess(df_design_log, func_dict_in, data_min_log, data_max_log)\n", "pp_results = preprocess(df_results_log, func_dict_in, data_min_log, data_max_log)" ] } ], "metadata": { "kernelspec": { "display_name": "ai", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 2 }