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      MUTC1750_Predict_Metrics.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "MUTC1750_Predict_Metrics.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "H6eOwX3UGpvG"
},
"source": [
"import pathlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"# ML models\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"# Multilayer Perceptron\n",
"from keras.models import Model\n",
"from keras.layers import Input\n",
"from keras.layers import Dense\n",
"from keras.layers import Dropout\n",
"from keras.layers.merge import concatenate\n",
"from tensorflow.keras import optimizers\n",
"from tensorflow.keras import backend\n",
"from keras.layers.advanced_activations import LeakyReLU, PReLU\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xO6mZcSAGyp7"
},
"source": [
"!git clone https://github.com/simsekergun/photodetectors.git\n",
"df = pd.read_csv(\"./photodetectors/MUTC1750designs.csv\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1qLZ_FkrHJJB"
},
"source": [
"# let's take log10 of doping levels so that we deal with numbers in the similar ranges\n",
"df[df.columns[22:40]] =np.log10(df[df.columns[22:40]])\n",
"df.shape"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "x5h23R0y1MXk"
},
"source": [
"df.head(3)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iyFCz4P3xSgt"
},
"source": [
"df.describe()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qBs0SZEek_Sh"
},
"source": [
"## Coefficient of Correlation\n",
"df[df.columns[0:5]].std()/df[df.columns[0:5]].mean()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lnIsvKeLIwZY"
},
"source": [
"# Let us some functions to normalize, de-normalize, and to calculate errors\n",
"def normx(x):\n",
" return (x - train_statsX['mean']) / train_statsX['std']\n",
"def norm(y):\n",
" return (y - train_statsY['mean']) / train_statsY['std']\n",
"def denorm(y):\n",
" return (y* train_statsY['std'] + train_statsY['mean']) \n",
"def mean_aep(u1,u2): \n",
" return (round(100*(100*sum(abs((u2-u1)/u1))/len(u1)))/100)\n",
"def max_aep(u1,u2): \n",
" return (round(100*(100*max(abs((u2-u1)/u1))))/100) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nAXW1rSrkJon"
},
"source": [
"# ANN parameters\n",
"ac = 'relu' # activation function\n",
"nnno = 48 # number of neurons\n",
"dr_rate = 0.2 # dropout rate\n",
"EPOCHS = 400 # number of epocs\n",
"LR = 0.001 # learning rate"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "T9UuySubf9K9"
},
"source": [
"for var_index in np.arange(5):\n",
" X_Train, X_Test, Y_Train, Y_Test = train_test_split(df.iloc[0:-1,5:40],df.iloc[0:-1,var_index], test_size=0.2, random_state=55)\n",
"\n",
" train_statsY = Y_Train.describe().transpose()\n",
" train_statsX = X_Train.describe().transpose()\n",
" XX = normx(X_Train)\n",
" YY = norm(Y_Train)\n",
" xx = normx(X_Test)\n",
" yy = norm(Y_Test)\n",
" #\n",
" visible = Input(shape=(len(X_Train.keys()),))\n",
" hidden1 = Dense(nnno, activation=ac)(visible)\n",
" hidden1 = Dropout(dr_rate)(hidden1)\n",
" hidden2 = Dense(nnno, activation=ac)(hidden1)\n",
" hidden2 = Dropout(dr_rate)(hidden2)\n",
" mergeA = concatenate([hidden2, visible])\n",
" hiddenB = Dense(nnno, activation=ac)(mergeA)\n",
" hiddenB = Dropout(dr_rate)(hiddenB)\n",
" hidden3 = Dense(nnno, activation=ac)(hiddenB)\n",
" hidden3 = Dropout(dr_rate)(hidden3)\n",
" merge = concatenate([hidden3, visible])\n",
" hidden4 = Dense(nnno, activation=ac)(merge)\n",
" hidden4 = Dropout(dr_rate)(hidden4)\n",
" predicted_value = Dense(1)(hidden4)\n",
" modelANN = Model(inputs=visible, outputs=predicted_value)\n",
" #\n",
" opt = optimizers.Adamax(learning_rate=LR)\n",
" modelANN.compile(optimizer=opt, loss=['mse'])\n",
" history = modelANN.fit(XX, YY,epochs=EPOCHS, validation_data = (xx,yy), verbose=0)\n",
" # plot losses\n",
" plt.figure(var_index+10)\n",
" plt.plot(history.history['loss'])\n",
" plt.plot(history.history['val_loss'])\n",
" plt.ylabel('loss')\n",
" plt.xlabel('epoch')\n",
" plt.legend(['train', 'test'], loc='upper right')\n",
" plt.show()\n",
" #\n",
" test_predictions = modelANN.predict(xx)\n",
" u1 = denorm(yy).to_numpy()\n",
" u2 = denorm(pd.Series(np.squeeze(test_predictions)))\n",
" # plot truth vs. prediction\n",
" x1 = min(min(u1),min(u2))\n",
" x2 = max(max(u1),max(u2))\n",
" plt.figure(var_index)\n",
" plt.plot([x1,x2],[x1,x2],color='red')\n",
" plt.scatter(u1, u2)\n",
" plt.xlabel('Ground Truth')\n",
" plt.ylabel('Prediction')\n",
" plt.gca().set_aspect('equal', adjustable='box')\n",
" plt.grid(color='grey', linestyle='--', linewidth=1)\n",
" # Errors\n",
" error_ANN, error_ANN_max = mean_aep(u1,u2), max_aep(u1,u2) \n",
" # Save ANN Results\n",
" if var_index == 0:\n",
" np.savetxt(\"MUTC_training_loss.csv\", history.history['loss'], delimiter=\",\")\n",
" np.savetxt(\"MUTC_testing_loss.csv\", history.history['val_loss'], delimiter=\",\")\n",
" np.savetxt(\"MUTC_phasenoise_truth.csv\", u1, delimiter=\",\")\n",
" np.savetxt(\"MUTC_phasenoise_predictions.csv\", u2, delimiter=\",\")\n",
" ## LINEAR REGRESSION\n",
" modelLR = LinearRegression()\n",
" modelLR.fit(XX, YY)\n",
" yhat = modelLR.predict(xx)\n",
" u2 = denorm(pd.Series(np.squeeze(yhat)))\n",
" # calculate errors\n",
" error_LR, error_LR_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
" ## k-Nearest Neighbors\n",
" modelkNN = KNeighborsRegressor()\n",
" modelkNN.fit(XX, YY)\n",
" yhat = modelkNN.predict(xx)\n",
" u2 = denorm(pd.Series(np.squeeze(yhat)))\n",
" # calculate errors\n",
" error_kNN, error_kNN_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
" ## RANDOM FOREST \n",
" modelRF = RandomForestRegressor()\n",
" modelRF.fit(XX, YY)\n",
" yhat = modelRF.predict(xx)\n",
" u2 = denorm(pd.Series(np.squeeze(yhat)))\n",
" # calculate errors\n",
" error_RF, error_RF_max = mean_aep(u1,u2), max_aep(u1,u2)\n",
" # PRINT ERRORS\n",
" print('************',var_index,'************')\n",
" print('Mean Absolute Percentage Errors: LR, kNN, RF, ANN')\n",
" print(error_LR, error_kNN, error_RF, error_ANN)\n",
" print('Max Absolute Percentage Errors: LR, kNN, RF, ANN')\n",
" print(error_LR_max, error_kNN_max, error_RF_max, error_ANN_max)\n",
" backend.clear_session()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FFtnWBnMmbAu"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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