From 8ef23b61e61df33e142f1a6d72ee0a720f460a18 Mon Sep 17 00:00:00 2001 From: Alejandro Montanez Date: Wed, 27 Jul 2022 15:37:36 +0200 Subject: [PATCH] Team 1: Avocados --- .../Classical_fair.ipynb | 828 + .../Data analysis.ipynb | 2077 + .../Data/sol15_qasm.npy | Bin 0 -> 1113790 bytes Quantum-Supply-Chain-Manager/Data/sol7.npy | Bin 0 -> 737768 bytes .../Images/1Layer.png | Bin 0 -> 68770 bytes .../Images/1Layer_noise.png | Bin 0 -> 68904 bytes Quantum-Supply-Chain-Manager/Images/CNN.png | Bin 0 -> 72848 bytes .../Images/CostFunc.png | Bin 0 -> 374801 bytes .../Images/GraphProblem.png | Bin 0 -> 137281 bytes .../Images/OptimalSolution.png | Bin 0 -> 92183 bytes .../Images/ProblemSolutions.png | Bin 0 -> 147594 bytes Quantum-Supply-Chain-Manager/Images/QSCM.png | Bin 0 -> 1505795 bytes .../Images/Results-Ansatz.png | Bin 0 -> 303176 bytes .../Images/SettingProblem.png | Bin 0 -> 917116 bytes .../Images/SettingTheProblem.png | Bin 0 -> 832964 bytes .../Images/Sol15Q.png | Bin 0 -> 81925 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create mode 100644 Quantum-Supply-Chain-Manager/quantum_model_MERA_1_layers_noise.ipynb diff --git a/Quantum-Supply-Chain-Manager/Classical_fair.ipynb b/Quantum-Supply-Chain-Manager/Classical_fair.ipynb new file mode 100644 index 0000000..280fa34 --- /dev/null +++ b/Quantum-Supply-Chain-Manager/Classical_fair.ipynb @@ -0,0 +1,828 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fair Model\n", + "\n", + "The fair model is designed from the data set that was made in the data analysis section and his can compare with the quantum models, for this will be with the use of neural networks to predict when there is a backorder and thus pass the data to the company responsible for the distribution and storage.\n", + "\n", + "\n", + "In case you don't have installed tensorflow uncomment the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install --upgrade pip\n", + "# !pip uninstall tensorflow --y\n", + "# !pip install tensorflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In case you dont want to work with GPU uncommenr the following cell" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '-1'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the labraries as pandas to load the csv file, numpy for the seed, keras and tensorflow the framework for generate the machine learning methods as neurnal networks, and sklearn for the metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# load csv file\n", + "import pandas as pd\n", + "\n", + "# numpy to the seed \n", + "import numpy as np\n", + "\n", + "# load csv fileframework to neural networks\n", + "import tensorflow as tf\n", + "\n", + "#Method forthe neural network\n", + "from keras.regularizers import l2\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout\n", + "\n", + "#save as image the model summary\n", + "from keras.utils.vis_utils import plot_model\n", + "\n", + "# librariesto plot\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline \n", + "import seaborn as sns\n", + "\n", + "from sklearn.metrics import confusion_matrix, roc_curve, auc\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# demonstration of calculating metrics for a neural network model using sklearn\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import cohen_kappa_score\n", + "from sklearn.metrics import roc_auc_score\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the train and test sets from the Data analysis module" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(((2000, 16), (2000,)), ((6159, 16), (6159,)))" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_train = pd.read_csv(\"fair_train.csv\")\n", + "X_train,y_train = data_train[data_train.columns[:16]].values, data_train[data_train.columns[16]].values\n", + "\n", + "data_test = pd.read_csv(\"classic_test.csv\")\n", + "X_test,y_test = data_test[data_test.columns[:16]].values, data_test[data_test.columns[16]].values\n", + "\n", + "(X_train.shape, y_train.shape),(X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Random seed for reproducibility\n", + "\n", + "In the same case of reproducing the code it is important to consider the numpy seed, for this purpose the following cell is used" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(123)\n", + "tf.random.set_seed(123)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For neuran networks is important to normalize and check the distribution of the variables for that exist the variable StandarScaler that \n", + " - Scaling by Normalization or min-max scaling\n", + " - Scaling by mean and standard deviation or standardization\n", + " \n", + "1. StandardScaler is a function used to standardize the data .\n", + "2. Standardized value for x is computed as (x-mean(column))/standard deviation(column). " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "scale = StandardScaler()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
StandardScaler()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "StandardScaler()" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scale.fit(X_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apply the StandardScaler to the train and test sets" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_std = scale.transform(X_train)\n", + "X_test_std = scale.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the values after that" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([-0.08878785, 0.99245064, -0.07217543, -0.12087906, -0.13114244,\n", + " -0.1312855 , -0.08861196, -0.08968266, -0.09519565, -0.10915324,\n", + " -0.07271837, -0.07898323, 0.27210453, 0.19214586, -0.09095412,\n", + " 0.3788279 ]),\n", + " 0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train_std[1], y_train[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The model \n", + "Using the same classicla model with a balance close to 50% for both classes." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-07-26 22:36:35.981185: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = Sequential()\n", + "\n", + "model.add(Dense(25, input_dim=16, activation='relu', kernel_regularizer=l2(1e-6),kernel_initializer=\"glorot_normal\"))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(8, activation='relu',kernel_regularizer=l2(1e-6), kernel_initializer=\"glorot_normal\"))\n", + "model.add(Dropout(0.5))\n", + "\n", + "model.add(Dense(1, activation='sigmoid',kernel_regularizer=l2(1e-6), kernel_initializer=\"glorot_normal\"))\n", + "\n", + "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since they are unbalanced values, it is common to deal with the Area under the ROC Curve(AUC)\n", + "\n", + "this works using True Positive Rate and False Positive Rate, where they are defined as :\n", + "\n", + "- **True Positive Rate (TPR)** is a synonym for recall and is therefore defined as follows:\n", + "$$\n", + "T P R=\\frac{T P}{T P+F N}\n", + "$$\n", + "- **False Positive Rate (FPR)** is defined as follows:\n", + "$$\n", + "F P R=\\frac{F P}{F P+T N}\n", + "$$\n", + "\n", + "metric instead of accuracy, therefore both were selected to train the model with the Adam optimizer. Using binary crossentropy because is a binary classification \n", + "\n", + "$$\\operatorname{Loss}=-\\frac{1}{\\begin{array}{c}\n", + "\\text { output } \\\\\n", + "\\text { size }\n", + "\\end{array}} \\sum_{i=1}^{\\substack{\\text { output } \\\\\n", + "\\text { size }}} y_{i} \\cdot \\log \\hat{y}_{i}+\\left(1-y_{i}\\right) \\cdot \\log \\left(1-\\hat{y}_{i}\\right).\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile model\n", + "auc = tf.keras.metrics.AUC()\n", + "model.compile(loss='binary_crossentropy', optimizer=\"Adam\", metrics=['accuracy',auc])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using 100 epochs a batch size of 32, with a validation split of 0.2 of the train data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "50/50 [==============================] - 1s 5ms/step - loss: 0.7900 - accuracy: 0.5556 - auc: 0.4721 - val_loss: 0.7819 - val_accuracy: 0.3125 - val_auc: 0.0000e+00\n", + "Epoch 2/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.7510 - accuracy: 0.5788 - auc: 0.5012 - val_loss: 0.7884 - val_accuracy: 0.2550 - val_auc: 0.0000e+00\n", + "Epoch 3/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.7148 - accuracy: 0.6056 - auc: 0.5008 - val_loss: 0.7906 - val_accuracy: 0.2450 - val_auc: 0.0000e+00\n", + "Epoch 4/100\n", + "50/50 [==============================] - 0s 925us/step - loss: 0.6828 - accuracy: 0.6181 - auc: 0.5406 - val_loss: 0.8101 - val_accuracy: 0.2175 - val_auc: 0.0000e+00\n", + "Epoch 5/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6929 - accuracy: 0.6156 - auc: 0.5438 - val_loss: 0.8379 - val_accuracy: 0.1800 - val_auc: 0.0000e+00\n", + "Epoch 6/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.7060 - accuracy: 0.6194 - auc: 0.5278 - val_loss: 0.8362 - val_accuracy: 0.1750 - val_auc: 0.0000e+00\n", + "Epoch 7/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6739 - accuracy: 0.6356 - auc: 0.5729 - val_loss: 0.8541 - val_accuracy: 0.1700 - val_auc: 0.0000e+00\n", + "Epoch 8/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6793 - accuracy: 0.6319 - auc: 0.5484 - val_loss: 0.8561 - val_accuracy: 0.1625 - val_auc: 0.0000e+00\n", + "Epoch 9/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6903 - accuracy: 0.6356 - auc: 0.5396 - val_loss: 0.8589 - val_accuracy: 0.1550 - val_auc: 0.0000e+00\n", + "Epoch 10/100\n", + "50/50 [==============================] - 0s 985us/step - loss: 0.6735 - accuracy: 0.6294 - auc: 0.5527 - val_loss: 0.8655 - val_accuracy: 0.1400 - val_auc: 0.0000e+00\n", + "Epoch 11/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6720 - accuracy: 0.6381 - auc: 0.5636 - val_loss: 0.8764 - val_accuracy: 0.1225 - val_auc: 0.0000e+00\n", + "Epoch 12/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6583 - accuracy: 0.6269 - auc: 0.5681 - val_loss: 0.8830 - val_accuracy: 0.1125 - val_auc: 0.0000e+00\n", + "Epoch 13/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6693 - accuracy: 0.6344 - auc: 0.5895 - val_loss: 0.8937 - val_accuracy: 0.1150 - val_auc: 0.0000e+00\n", + "Epoch 14/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6545 - accuracy: 0.6419 - auc: 0.5780 - val_loss: 0.8968 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 15/100\n", + "50/50 [==============================] - 0s 931us/step - loss: 0.6616 - accuracy: 0.6369 - auc: 0.5757 - val_loss: 0.8906 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 16/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6418 - accuracy: 0.6494 - auc: 0.5974 - val_loss: 0.8928 - val_accuracy: 0.1175 - val_auc: 0.0000e+00\n", + "Epoch 17/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6547 - accuracy: 0.6300 - auc: 0.5886 - val_loss: 0.9022 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 18/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6508 - accuracy: 0.6406 - auc: 0.5902 - val_loss: 0.9179 - val_accuracy: 0.0900 - val_auc: 0.0000e+00\n", + "Epoch 19/100\n", + "50/50 [==============================] - 0s 939us/step - loss: 0.6498 - accuracy: 0.6456 - auc: 0.5855 - val_loss: 0.9025 - val_accuracy: 0.1025 - val_auc: 0.0000e+00\n", + "Epoch 20/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6466 - accuracy: 0.6356 - auc: 0.6081 - val_loss: 0.9184 - val_accuracy: 0.1025 - val_auc: 0.0000e+00\n", + "Epoch 21/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6414 - accuracy: 0.6463 - auc: 0.6011 - val_loss: 0.9119 - val_accuracy: 0.1050 - val_auc: 0.0000e+00\n", + "Epoch 22/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6445 - accuracy: 0.6338 - auc: 0.6216 - val_loss: 0.9223 - val_accuracy: 0.1075 - val_auc: 0.0000e+00\n", + "Epoch 23/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6397 - accuracy: 0.6456 - auc: 0.6131 - val_loss: 0.9237 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 24/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6402 - accuracy: 0.6388 - auc: 0.6138 - val_loss: 0.9288 - val_accuracy: 0.1175 - val_auc: 0.0000e+00\n", + "Epoch 25/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6430 - accuracy: 0.6419 - auc: 0.6146 - val_loss: 0.9257 - val_accuracy: 0.1200 - val_auc: 0.0000e+00\n", + "Epoch 26/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6376 - accuracy: 0.6431 - auc: 0.6155 - val_loss: 0.9242 - val_accuracy: 0.1200 - val_auc: 0.0000e+00\n", + "Epoch 27/100\n", + "50/50 [==============================] - 0s 984us/step - loss: 0.6414 - accuracy: 0.6381 - auc: 0.6245 - val_loss: 0.9246 - val_accuracy: 0.1250 - val_auc: 0.0000e+00\n", + "Epoch 28/100\n", + "50/50 [==============================] - 0s 921us/step - loss: 0.6313 - accuracy: 0.6525 - auc: 0.6421 - val_loss: 0.9392 - val_accuracy: 0.1050 - val_auc: 0.0000e+00\n", + "Epoch 29/100\n", + "50/50 [==============================] - 0s 971us/step - loss: 0.6321 - accuracy: 0.6438 - auc: 0.6332 - val_loss: 0.9407 - val_accuracy: 0.1200 - val_auc: 0.0000e+00\n", + "Epoch 30/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6452 - accuracy: 0.6425 - auc: 0.6086 - val_loss: 0.9394 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 31/100\n", + "50/50 [==============================] - 0s 852us/step - loss: 0.6378 - accuracy: 0.6469 - auc: 0.6224 - val_loss: 0.9324 - val_accuracy: 0.0975 - val_auc: 0.0000e+00\n", + "Epoch 32/100\n", + "50/50 [==============================] - 0s 941us/step - loss: 0.6303 - accuracy: 0.6500 - auc: 0.6382 - val_loss: 0.9405 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 33/100\n", + "50/50 [==============================] - 0s 949us/step - loss: 0.6330 - accuracy: 0.6475 - auc: 0.6408 - val_loss: 0.9412 - val_accuracy: 0.1250 - val_auc: 0.0000e+00\n", + "Epoch 34/100\n", + "50/50 [==============================] - 0s 968us/step - loss: 0.6406 - accuracy: 0.6369 - auc: 0.6385 - val_loss: 0.9325 - val_accuracy: 0.1225 - val_auc: 0.0000e+00\n", + "Epoch 35/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6299 - accuracy: 0.6469 - auc: 0.6475 - val_loss: 0.9308 - val_accuracy: 0.1325 - val_auc: 0.0000e+00\n", + "Epoch 36/100\n", + "50/50 [==============================] - 0s 937us/step - loss: 0.6308 - accuracy: 0.6406 - auc: 0.6420 - val_loss: 0.9450 - val_accuracy: 0.1100 - val_auc: 0.0000e+00\n", + "Epoch 37/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6337 - accuracy: 0.6494 - auc: 0.6395 - val_loss: 0.9366 - val_accuracy: 0.1125 - val_auc: 0.0000e+00\n", + "Epoch 38/100\n", + "50/50 [==============================] - 0s 977us/step - loss: 0.6381 - accuracy: 0.6388 - auc: 0.6302 - val_loss: 0.9399 - val_accuracy: 0.1000 - val_auc: 0.0000e+00\n", + "Epoch 39/100\n", + "50/50 [==============================] - 0s 996us/step - loss: 0.6338 - accuracy: 0.6419 - auc: 0.6477 - val_loss: 0.9522 - val_accuracy: 0.0725 - val_auc: 0.0000e+00\n", + "Epoch 40/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6321 - accuracy: 0.6506 - auc: 0.6314 - val_loss: 0.9572 - val_accuracy: 0.0775 - val_auc: 0.0000e+00\n", + "Epoch 41/100\n", + "50/50 [==============================] - 0s 942us/step - loss: 0.6328 - accuracy: 0.6375 - auc: 0.6317 - val_loss: 0.9386 - val_accuracy: 0.1050 - val_auc: 0.0000e+00\n", + "Epoch 42/100\n", + "50/50 [==============================] - 0s 974us/step - loss: 0.6275 - accuracy: 0.6494 - auc: 0.6513 - val_loss: 0.9338 - val_accuracy: 0.1125 - val_auc: 0.0000e+00\n", + "Epoch 43/100\n", + "50/50 [==============================] - 0s 881us/step - loss: 0.6296 - accuracy: 0.6394 - auc: 0.6471 - val_loss: 0.9417 - val_accuracy: 0.1250 - val_auc: 0.0000e+00\n", + "Epoch 44/100\n", + "50/50 [==============================] - 0s 916us/step - loss: 0.6283 - accuracy: 0.6406 - auc: 0.6440 - val_loss: 0.9506 - val_accuracy: 0.1450 - val_auc: 0.0000e+00\n", + "Epoch 45/100\n", + "50/50 [==============================] - 0s 971us/step - loss: 0.6229 - accuracy: 0.6538 - auc: 0.6487 - val_loss: 0.9303 - val_accuracy: 0.1750 - val_auc: 0.0000e+00\n", + "Epoch 46/100\n", + "50/50 [==============================] - 0s 920us/step - loss: 0.6324 - accuracy: 0.6538 - auc: 0.6379 - val_loss: 0.9146 - val_accuracy: 0.1800 - val_auc: 0.0000e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 47/100\n", + "50/50 [==============================] - 0s 943us/step - loss: 0.6226 - accuracy: 0.6525 - auc: 0.6539 - val_loss: 0.9226 - val_accuracy: 0.1800 - val_auc: 0.0000e+00\n", + "Epoch 48/100\n", + "50/50 [==============================] - 0s 883us/step - loss: 0.6185 - accuracy: 0.6519 - auc: 0.6671 - val_loss: 0.9242 - val_accuracy: 0.1925 - val_auc: 0.0000e+00\n", + "Epoch 49/100\n", + "50/50 [==============================] - 0s 987us/step - loss: 0.6222 - accuracy: 0.6650 - auc: 0.6532 - val_loss: 0.9066 - val_accuracy: 0.1925 - val_auc: 0.0000e+00\n", + "Epoch 50/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6307 - accuracy: 0.6406 - auc: 0.6424 - val_loss: 0.9126 - val_accuracy: 0.1875 - val_auc: 0.0000e+00\n", + "Epoch 51/100\n", + "50/50 [==============================] - 0s 984us/step - loss: 0.6216 - accuracy: 0.6594 - auc: 0.6551 - val_loss: 0.9318 - val_accuracy: 0.1800 - val_auc: 0.0000e+00\n", + "Epoch 52/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6218 - accuracy: 0.6500 - auc: 0.6575 - val_loss: 0.9431 - val_accuracy: 0.1675 - val_auc: 0.0000e+00\n", + "Epoch 53/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6258 - accuracy: 0.6569 - auc: 0.6468 - val_loss: 0.9201 - val_accuracy: 0.1725 - val_auc: 0.0000e+00\n", + "Epoch 54/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6268 - accuracy: 0.6513 - auc: 0.6483 - val_loss: 0.9188 - val_accuracy: 0.1725 - val_auc: 0.0000e+00\n", + "Epoch 55/100\n", + "50/50 [==============================] - 0s 924us/step - loss: 0.6269 - accuracy: 0.6450 - auc: 0.6560 - val_loss: 0.9255 - val_accuracy: 0.1575 - val_auc: 0.0000e+00\n", + "Epoch 56/100\n", + "50/50 [==============================] - 0s 993us/step - loss: 0.6154 - accuracy: 0.6637 - auc: 0.6645 - val_loss: 0.9294 - val_accuracy: 0.1750 - val_auc: 0.0000e+00\n", + "Epoch 57/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6202 - 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0.2100 - val_auc: 0.0000e+00\n", + "Epoch 68/100\n", + "50/50 [==============================] - 0s 891us/step - loss: 0.6067 - accuracy: 0.6600 - auc: 0.6927 - val_loss: 0.8971 - val_accuracy: 0.2025 - val_auc: 0.0000e+00\n", + "Epoch 69/100\n", + "50/50 [==============================] - 0s 999us/step - loss: 0.6168 - accuracy: 0.6531 - auc: 0.6624 - val_loss: 0.9056 - val_accuracy: 0.2025 - val_auc: 0.0000e+00\n", + "Epoch 70/100\n", + "50/50 [==============================] - 0s 955us/step - loss: 0.6119 - accuracy: 0.6488 - auc: 0.6794 - val_loss: 0.9053 - val_accuracy: 0.1950 - val_auc: 0.0000e+00\n", + "Epoch 71/100\n", + "50/50 [==============================] - 0s 964us/step - loss: 0.6074 - accuracy: 0.6644 - auc: 0.6822 - val_loss: 0.9026 - val_accuracy: 0.1975 - val_auc: 0.0000e+00\n", + "Epoch 72/100\n", + "50/50 [==============================] - 0s 972us/step - loss: 0.6141 - accuracy: 0.6594 - auc: 0.6694 - val_loss: 0.9244 - val_accuracy: 0.1800 - val_auc: 0.0000e+00\n", + "Epoch 73/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6055 - accuracy: 0.6550 - auc: 0.6794 - val_loss: 0.9116 - val_accuracy: 0.1950 - val_auc: 0.0000e+00\n", + "Epoch 74/100\n", + "50/50 [==============================] - 0s 928us/step - loss: 0.6060 - accuracy: 0.6650 - auc: 0.6859 - val_loss: 0.9100 - val_accuracy: 0.2125 - val_auc: 0.0000e+00\n", + "Epoch 75/100\n", + "50/50 [==============================] - 0s 985us/step - loss: 0.6077 - accuracy: 0.6619 - auc: 0.6841 - val_loss: 0.9043 - val_accuracy: 0.2175 - val_auc: 0.0000e+00\n", + "Epoch 76/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5996 - accuracy: 0.6706 - auc: 0.6940 - val_loss: 0.8941 - val_accuracy: 0.2125 - val_auc: 0.0000e+00\n", + "Epoch 77/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6099 - accuracy: 0.6562 - auc: 0.6836 - val_loss: 0.9068 - val_accuracy: 0.2050 - val_auc: 0.0000e+00\n", + "Epoch 78/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6037 - accuracy: 0.6631 - auc: 0.6994 - val_loss: 0.9070 - val_accuracy: 0.2100 - val_auc: 0.0000e+00\n", + "Epoch 79/100\n", + "50/50 [==============================] - 0s 918us/step - loss: 0.6056 - accuracy: 0.6662 - auc: 0.6879 - val_loss: 0.9047 - val_accuracy: 0.2150 - val_auc: 0.0000e+00\n", + "Epoch 80/100\n", + "50/50 [==============================] - 0s 979us/step - loss: 0.6007 - accuracy: 0.6612 - auc: 0.6960 - val_loss: 0.8826 - val_accuracy: 0.2075 - val_auc: 0.0000e+00\n", + "Epoch 81/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5941 - accuracy: 0.6619 - auc: 0.7098 - val_loss: 0.8903 - val_accuracy: 0.2075 - val_auc: 0.0000e+00\n", + "Epoch 82/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.6027 - accuracy: 0.6625 - auc: 0.6920 - val_loss: 0.8758 - val_accuracy: 0.2050 - val_auc: 0.0000e+00\n", + "Epoch 83/100\n", + "50/50 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0s 1ms/step - loss: 0.5970 - accuracy: 0.6769 - auc: 0.6915 - val_loss: 0.8696 - val_accuracy: 0.2200 - val_auc: 0.0000e+00\n", + "Epoch 89/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5965 - accuracy: 0.6769 - auc: 0.7089 - val_loss: 0.8699 - val_accuracy: 0.2275 - val_auc: 0.0000e+00\n", + "Epoch 90/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5909 - accuracy: 0.6744 - auc: 0.7120 - val_loss: 0.8797 - val_accuracy: 0.2250 - val_auc: 0.0000e+00\n", + "Epoch 91/100\n", + "50/50 [==============================] - 0s 967us/step - loss: 0.5919 - accuracy: 0.6662 - auc: 0.7109 - val_loss: 0.8753 - val_accuracy: 0.2275 - val_auc: 0.0000e+00\n", + "Epoch 92/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5972 - accuracy: 0.6650 - auc: 0.7005 - val_loss: 0.8778 - val_accuracy: 0.2175 - val_auc: 0.0000e+00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 93/100\n", + "50/50 [==============================] - 0s 859us/step - loss: 0.5982 - accuracy: 0.6587 - auc: 0.7104 - val_loss: 0.8648 - val_accuracy: 0.2225 - val_auc: 0.0000e+00\n", + "Epoch 94/100\n", + "50/50 [==============================] - 0s 900us/step - loss: 0.5874 - accuracy: 0.6781 - auc: 0.7151 - val_loss: 0.8816 - val_accuracy: 0.2300 - val_auc: 0.0000e+00\n", + "Epoch 95/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5781 - accuracy: 0.6869 - auc: 0.7294 - val_loss: 0.8404 - val_accuracy: 0.2350 - val_auc: 0.0000e+00\n", + "Epoch 96/100\n", + "50/50 [==============================] - 0s 979us/step - loss: 0.5816 - accuracy: 0.6706 - auc: 0.7241 - val_loss: 0.8642 - val_accuracy: 0.2375 - val_auc: 0.0000e+00\n", + "Epoch 97/100\n", + "50/50 [==============================] - 0s 938us/step - loss: 0.5828 - accuracy: 0.6781 - auc: 0.7192 - val_loss: 0.8466 - val_accuracy: 0.2425 - val_auc: 0.0000e+00\n", + "Epoch 98/100\n", + "50/50 [==============================] - 0s 912us/step - loss: 0.5835 - accuracy: 0.6850 - auc: 0.7225 - val_loss: 0.8818 - val_accuracy: 0.2725 - val_auc: 0.0000e+00\n", + "Epoch 99/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5849 - accuracy: 0.6819 - auc: 0.7170 - val_loss: 0.8112 - val_accuracy: 0.3275 - val_auc: 0.0000e+00\n", + "Epoch 100/100\n", + "50/50 [==============================] - 0s 1ms/step - loss: 0.5854 - accuracy: 0.6744 - auc: 0.7210 - val_loss: 0.8544 - val_accuracy: 0.2550 - val_auc: 0.0000e+00\n" + ] + } + ], + "source": [ + "model_history = model.fit(X_train_std, y_train, epochs=100,\n", + " batch_size=32, \n", + " validation_split=0.2, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predictions\n", + "\n", + "After that we used the predictions of the train and test set" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_pred = model.predict(X_train_std)\n", + "test_pred = model.predict(X_test_std)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "to convert the predict values into the classes was designed an condition that looks as " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "y_train_pred = (model.predict(X_train_std) > 0.5).astype(\"int32\")\n", + "y_test_pred = (model.predict(X_test_std) > 0.5).astype(\"int32\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Getting evaluation metrics and evaluating model performance\n", + "\n", + "Using different metrics as \n", + "\n", + "- accuracy\n", + "- precision\n", + "- recall\n", + "- f1 score\n", + "- cohen_kappa\n", + "- roc_auc\n", + "\n", + "\n", + "The results of the train are important to check the F1 score and ROC AUC, they are \n", + "\n", + "- F1 score: 0.413999\n", + "- ROC AUC: 0.606500\n", + "\n", + "they are worst with respect the imbalance but good to comapre with a quantum model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.606500\n", + "Precision: 0.810496\n", + "Recall: 0.278000\n", + "F1 score: 0.413999\n", + "Cohens kappa: 0.213000\n", + "ROC AUC: 0.606500\n", + "[[935 65]\n", + " [722 278]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "accuracy = accuracy_score(y_train, y_train_pred)\n", + "print('Accuracy: %f' % accuracy)\n", + "# precision tp / (tp + fp)\n", + "precision = precision_score(y_train, y_train_pred)\n", + "print('Precision: %f' % precision)\n", + "# recall: tp / (tp + fn)\n", + "recall = recall_score(y_train, y_train_pred)\n", + "print('Recall: %f' % recall)\n", + "# f1: 2 tp / (2 tp + fp + fn)\n", + "f1 = f1_score(y_train, y_train_pred)\n", + "print('F1 score: %f' % f1)\n", + " \n", + "# kappa\n", + "kappa = cohen_kappa_score(y_train, y_train_pred)\n", + "print('Cohens kappa: %f' % kappa)\n", + "# ROC AUC\n", + "auc = roc_auc_score(y_train, y_train_pred)\n", + "print('ROC AUC: %f' % auc)\n", + "# confusion matrix\n", + "train_matrix = confusion_matrix(y_train, y_train_pred)\n", + "print(train_matrix)\n", + "ax = sns.heatmap(train_matrix, annot=True, cmap='Blues', fmt='g')\n", + "\n", + "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n", + "ax.set_xlabel('\\nPredicted Values')\n", + "ax.set_ylabel('Actual Values ');\n", + "\n", + "ax.xaxis.set_ticklabels(['0','1'])\n", + "ax.yaxis.set_ticklabels(['0','1'])\n", + "\n", + "## Display the visualization of the Confusion Matrix.\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results of the test are important to check the F1 score and ROC AUC, they are \n", + "\n", + "- F1 score: 0.338028\n", + "- ROC AUC: 0.598467\n", + "\n", + "they are worst with respect the imbalance but good to comapre with a quantum model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.809222\n", + "Precision: 0.464396\n", + "Recall: 0.265722\n", + "F1 score: 0.338028\n", + "Cohens kappa: 0.236103\n", + "ROC AUC: 0.598467\n", + "[[4684 346]\n", + " [ 829 300]]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "accuracy = accuracy_score(y_test, y_test_pred)\n", + "print('Accuracy: %f' % accuracy)\n", + "# precision tp / (tp + fp)\n", + "precision = precision_score(y_test, y_test_pred)\n", + "print('Precision: %f' % precision)\n", + "# recall: tp / (tp + fn)\n", + "recall = recall_score(y_test, y_test_pred)\n", + "print('Recall: %f' % recall)\n", + "# f1: 2 tp / (2 tp + fp + fn)\n", + "f1 = f1_score(y_test, y_test_pred)\n", + "print('F1 score: %f' % f1)\n", + " \n", + "# kappa\n", + "kappa = cohen_kappa_score(y_test, y_test_pred)\n", + "print('Cohens kappa: %f' % kappa)\n", + "# ROC AUC\n", + "auc = roc_auc_score(y_test, y_test_pred)\n", + "print('ROC AUC: %f' % auc)\n", + "# confusion matrix\n", + "test_matrix = confusion_matrix(y_test, y_test_pred)\n", + "print(test_matrix)\n", + "ax = sns.heatmap(test_matrix, annot=True, cmap='Blues', fmt='g')\n", + "\n", + "ax.set_title('Seaborn Confusion Matrix with labels\\n\\n');\n", + "ax.set_xlabel('\\nPredicted Values')\n", + "ax.set_ylabel('Actual Values ');\n", + "\n", + "ax.xaxis.set_ticklabels(['0','1'])\n", + "ax.yaxis.set_ticklabels(['0','1'])\n", + "\n", + "## Display the visualization of the Confusion Matrix.\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.13" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Quantum-Supply-Chain-Manager/Data analysis.ipynb b/Quantum-Supply-Chain-Manager/Data analysis.ipynb new file mode 100644 index 0000000..c05fb15 --- /dev/null +++ b/Quantum-Supply-Chain-Manager/Data analysis.ipynb @@ -0,0 +1,2077 @@ +{ + "cells": [ + { + "attachments": { + "backorder-600x300.jpg": { + "image/jpeg": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction \n", + "\n", + "\n", + "The problem being considered in this section is the prediction of a **backorder**. This is about an order that you cannot be fulfilled at a given time because there is not enough inventory or the item is out of stock, but you can guarantee delivery of the product in the future. Unlike the out-of-stock situation, in the backorder situation customers can purchase the items and place the order as they will be guaranteed delivery in the future, i.e., it is an order with a delayed delivery date.\n", + "\n", + "![backorder-600x300.jpg](attachment:backorder-600x300.jpg)\n", + "\n", + "
Fig 1. Product storage (Figure obtained from here\n", + ").
\n" + ] + }, + { + "attachments": { + "WooCommerce-backorder-product-table-770x155.jpg": { + "image/jpeg": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## why backorders are a real problem?\n", + "\n", + "For many companies and businesses currently spread their products online with them there are platforms like [Amazon](https://www.amazon.com/) that helps some to share the products, but these tend to be run by intermediaries, ie people who manage to sell you the item and they communicate with suppliers. All situations that can happen in some cases will generate the back order, but as this affects, there are some important points such as \n", + "\n", + "- Increased demand for the products they offer. This can affect if you have a bad sales forecasting system or there are factors that cannot be controlled due to extraordinary situations that change the demand for certain products.\n", + " \n", + "- There is a bottleneck in the supply chain, e.g. too few suppliers or communication failures.\n", + "\n", + "- Poor inventory management and lack of control over product storage. \n", + "\n", + "![WooCommerce-backorder-product-table-770x155.jpg](attachment:WooCommerce-backorder-product-table-770x155.jpg)\n", + "
Fig 2. Example of backorder (Figure obtained from here\n", + ").
\n", + "\n", + "\n", + "## How to avoid backorders?\n", + "\n", + "Manage the supply chain, warehousing and inventory where the number of products should be considered and predicted so as not to increase the cost of storage, and therefore not to affect the prices of the products.\n", + "\n", + "\n", + "This work is based on the Predict Product Backorders dataset. Originally performed by a Kaggle competition, in which we are going to work with a sample for our experiments and results.\n", + "\n", + "\n", + "## Proposal: Backorder prediction system\n", + "\n", + "Backorders are unavoidable, but thanks to the prediction of the items that are considered in the backorder planning can be optimized in order to avoid costs, increase the production load, support logistics and support transportation planning to decrease costs and times.\n", + "\n", + "\n", + "# Data set\n", + "\n", + "Many companies produce a large amount of structured data for each month of operation and this generates a large amount of historical data, using this data correctly in order to design a predictive model to forecast the order backlog and plan accordingly. \n", + "\n", + "For the acquisition of this data we used the dataset of a challenge of the [kaggle](https://www.kaggle.com/competitions/untadta/overview/evaluation), which can be found openly on [this github repository](https://github.com/rammel/DL).\n", + "\n", + "**This dataset has some variables with missing data of 7% and with an unbalance where one class is 81% and the other 19%.**\n", + "\n", + "# Data analysis\n", + "\n", + "For this project, before thinking about generating a prediction model, it is important to identify, highlight and analyze the data set, based on its characteristics and limitations.\n", + "\n", + "## Implementation Code\n", + "\n", + "Code used for data analysis and conditioning it for the model to be used.\n", + "\n", + "\n", + "### Check the dataset \n", + "\n", + "Data file contains the historical data for the 8 weeks prior to the week we are trying to predict. The dataset contains 23 variables including\n", + "\n", + "| | | | | | |\n", + "| --- | --- | --- | - | - | - |\n", + "| **sku** Product ID | **national_inv** Current inventory level for the part |**lead_time** Transit time for product | **in_transit_qty** Amount of product in transit from source|**forecast_3_month** Forecast sales for the next 3 months | **forecast_6_month** Forecast sales for the next 6 months|\n", + "|**forecast_9_month** Forecast sales for the next 9 months|**sales_1_month** Sales quantity for the prior 1 month time period|**sales_3_month** Sales quantity for the prior 3 month time period |**sales_6_month** Sales quantity for the prior 6 month time period |**sales_9_month** Sales quantity for the prior 9 month time period |**min_bank** Minimum recommend amount to stock \n", + "|**potential_issue** Source issue for part identified |**pieces_past_due** Parts overdue from source |**perf_6_month_avg** Source performance for prior 6 month period |**perf_12_month_avg** Source performance for prior 12 month period |**local_bo_qty** Amount of stock orders overdue|**deck_risk** Part risk flag\n", + "|**oe_constraint** Part risk flag | **ppap_risk** Part risk flag | **stop_auto_buy** Part risk flag | **rev_stop** Part risk flag| **went_on_backorder** – Product actually went on backorder. This is the target value." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Pre-processing\n", + "\n", + "Is important consider different libraries, to save and load csv files, with matemathical operation as pandas, numpy. Skelearn in case you want to split the dataset in train and test set and normalization of the sets. Matplotlib to show the different graphs and plots about the dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# to use dataframe and load csv file\n", + "import pandas as pd\n", + "\n", + "# to use for mathematical operations \n", + "import numpy as np\n", + "\n", + "# split the set in 2 set, common train and test\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "# plot different designs\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Random seed for reproducibility\n", + "\n", + "In case of reproducing the code it is important to consider the numpy seed, for this purpose the following cell is used" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(123)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Loading the data\n", + "\n", + "Consider the dataset in a csv called BackOrders" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data = pd.read_csv(\"dataset/BackOrders.csv\",header=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The size of this data set are 23 variables wtih 61589 items" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(61589, 23)" + ] + }, + "execution_count": 185, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To confirm that they are the same database, the header of the 23 columns that are the titles and the first 5 items" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sku national_inv lead_time in_transit_qty forecast_3_month \\\n", + "0 1888279 117 NaN 0 0 \n", + "1 1870557 7 2.0 0 0 \n", + "2 1475481 258 15.0 10 10 \n", + "3 1758220 46 2.0 0 0 \n", + "4 1360312 2 2.0 0 4 \n", + "\n", + " forecast_6_month forecast_9_month sales_1_month sales_3_month \\\n", + "0 0 0 0 0 \n", + "1 0 0 0 0 \n", + "2 77 184 46 132 \n", + "3 0 0 1 2 \n", + "4 6 10 2 2 \n", + "\n", + " sales_6_month ... pieces_past_due perf_6_month_avg perf_12_month_avg \\\n", + "0 15 ... 0 -99.00 -99.00 \n", + "1 0 ... 0 0.50 0.28 \n", + "2 256 ... 0 0.54 0.70 \n", + "3 6 ... 0 0.75 0.90 \n", + "4 5 ... 0 0.97 0.92 \n", + "\n", + " local_bo_qty deck_risk oe_constraint ppap_risk stop_auto_buy rev_stop \\\n", + "0 0 No No Yes Yes No \n", + "1 0 Yes No No Yes No \n", + "2 0 No No No Yes No \n", + "3 0 Yes No No Yes No \n", + "4 0 No No No Yes No \n", + "\n", + " went_on_backorder \n", + "0 No \n", + "1 No \n", + "2 No \n", + "3 No \n", + "4 No \n", + "\n", + "[5 rows x 23 columns]" + ] + }, + "execution_count": 186, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The raw data without any processing has 7 categorical variables, at least one variable with missing values and the variables perf_6_month_avg, perf_12_month_avg have a negative value that causes conflict.\n", + "\n", + "the next code replace the categorical in a binary value 0/1." + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [], + "source": [ + "for col in ['potential_issue',\n", + " 'deck_risk',\n", + " 'oe_constraint',\n", + " 'ppap_risk',\n", + " 'stop_auto_buy',\n", + " 'rev_stop',\n", + " 'went_on_backorder']: \n", + " data[col]=pd.factorize(data[col])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sku national_inv lead_time in_transit_qty \\\n", + "count 6.158900e+04 61589.000000 58186.000000 61589.000000 \n", + "mean 2.037188e+06 287.721882 7.559619 30.192843 \n", + "std 6.564178e+05 4233.906931 6.498952 792.869253 \n", + "min 1.068628e+06 -2999.000000 0.000000 0.000000 \n", + "25% 1.498574e+06 3.000000 4.000000 0.000000 \n", + "50% 1.898033e+06 10.000000 8.000000 0.000000 \n", + "75% 2.314826e+06 57.000000 8.000000 0.000000 \n", + "max 3.284895e+06 673445.000000 52.000000 170976.000000 \n", + "\n", + " forecast_3_month forecast_6_month forecast_9_month sales_1_month \\\n", + "count 6.158900e+04 6.158900e+04 6.158900e+04 61589.000000 \n", + "mean 1.692728e+02 3.150413e+02 4.535760e+02 44.742957 \n", + "std 5.286742e+03 9.774362e+03 1.420201e+04 1373.805831 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 \n", + "25% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 \n", + "50% 0.000000e+00 0.000000e+00 0.000000e+00 0.000000 \n", + "75% 1.200000e+01 2.500000e+01 3.600000e+01 6.000000 \n", + "max 1.126656e+06 2.094336e+06 3.062016e+06 295197.000000 \n", + "\n", + " sales_3_month sales_6_month ... pieces_past_due perf_6_month_avg \\\n", + "count 61589.000000 6.158900e+04 ... 61589.000000 61589.000000 \n", + "mean 150.732631 2.835465e+02 ... 1.605400 -6.264182 \n", + "std 5224.959649 8.872270e+03 ... 42.309229 25.537906 \n", + "min 0.000000 0.000000e+00 ... 0.000000 -99.000000 \n", + "25% 0.000000 0.000000e+00 ... 0.000000 0.620000 \n", + "50% 2.000000 4.000000e+00 ... 0.000000 0.820000 \n", + "75% 17.000000 3.400000e+01 ... 0.000000 0.960000 \n", + "max 934593.000000 1.799099e+06 ... 7392.000000 1.000000 \n", + "\n", + " perf_12_month_avg local_bo_qty deck_risk oe_constraint \\\n", + "count 61589.000000 61589.000000 61589.000000 61589.000000 \n", + "mean -5.863664 1.205361 0.218286 0.000195 \n", + "std 24.844514 29.981155 0.413086 0.013957 \n", + "min -99.000000 0.000000 0.000000 0.000000 \n", + "25% 0.640000 0.000000 0.000000 0.000000 \n", + "50% 0.800000 0.000000 0.000000 0.000000 \n", + "75% 0.950000 0.000000 0.000000 0.000000 \n", + "max 1.000000 2999.000000 1.000000 1.000000 \n", + "\n", + " ppap_risk stop_auto_buy rev_stop went_on_backorder \n", + "count 61589.000000 61589.000000 61589.000000 61589.000000 \n", + "mean 0.873403 0.037117 0.000325 0.183361 \n", + "std 0.332524 0.189050 0.018018 0.386965 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 1.000000 0.000000 0.000000 0.000000 \n", + "50% 1.000000 0.000000 0.000000 0.000000 \n", + "75% 1.000000 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 \n", + "\n", + "[8 rows x 23 columns]" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe(include='all')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Is important replace the negative value -99 for the variables perf_6_month_avg, perf_12_month_avg; and consider missing value." + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [], + "source": [ + "data['perf_6_month_avg']=data['perf_6_month_avg'].replace(-99, np.NaN)\n", + "data['perf_12_month_avg']=data['perf_12_month_avg'].replace(-99, np.NaN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to check the characteristics and whether they interact with each other, a correlation matrix is made, and following this matrix it is found that 5 variables do not affect the others." + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "varnames=list(data)[1:] \n", + "correlations = data[varnames].corr()\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "cax = ax.matshow(correlations, vmin=-1, vmax=1)\n", + "fig.colorbar(cax)\n", + "ticks = np.arange(0,22,1)\n", + "ax.set_xticks(ticks)\n", + "ax.set_yticks(ticks)\n", + "ax.set_xticklabels(varnames,rotation=90)\n", + "ax.set_yticklabels(varnames)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 5 variables that are of lesser importance and can be eliminated are \n", + "\n", + "- rev_stop\n", + "- oe_constraint\n", + "- potential_issue\n", + "- stop_auto_buy\n", + "- deck_risk\n" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "data.drop('rev_stop', axis=1, inplace=True)\n", + "data.drop('oe_constraint', axis=1, inplace=True)\n", + "data.drop('potential_issue', axis=1, inplace=True)\n", + "data.drop('stop_auto_buy', axis=1, inplace=True)\n", + "data.drop('deck_risk', axis=1, inplace=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the percentage of missing values found in the dataset, use the method `check_missing(data)`. We can see that there are only 3 variables with the highest loss being perf_6_month_avg with 7%, followed by perf_12_month_avg with 6.6% and lead_time with 5.5%." + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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perf_6_month_avg43417.0
perf_12_month_avg40916.6
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" + ], + "text/plain": [ + " Missing Percent\n", + "perf_6_month_avg 4341 7.0\n", + "perf_12_month_avg 4091 6.6\n", + "lead_time 3403 5.5" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def check_missing(data):\n", + " tot = data.isnull().sum().sort_values(ascending=False)\n", + " perc = ( round(100*data.isnull().sum()/data.isnull().count(),1) ).sort_values(ascending=False)\n", + " missing_data = pd.concat([tot, perc], axis=1, keys=['Missing', 'Percent'])\n", + " return missing_data[:3]\n", + " \n", + "check_missing(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this purpose, we will impute the variables and pass the median to these 3 variables in order to keep them correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_1, X_test, y_train_1, y_test = train_test_split(X, y, test_size=0.1, random_state=123, stratify = data['went_on_backorder'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the dimension of the train and test sets." + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(55430, 16)\n", + "(6159, 16)\n" + ] + } + ], + "source": [ + "print(X_train_1.shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is an imbalance of classes where approximately 81% belong to class 0 and 19% to class 1." + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 50.406665\n", + "0 49.593335\n", + "dtype: float64\n", + "0 81.669102\n", + "1 18.330898\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "print(pd.value_counts(y_train)/y_train.size * 100)\n", + "print(pd.value_counts(y_test)/y_test.size * 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To balance the training data a function is generated that reduces in two ways one to approximate a proportion to the test data, since there is a tendency that there will always be few backorder cases, and one with just the same number of classes for training." + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "def balance_split(X_train_1,y_train_1,flag=\"\"):\n", + " X_train0 = []\n", + " X_train1 = []\n", + "\n", + " y_train0 = []\n", + " y_train1 = []\n", + "\n", + " for i in range(len(y_train_1)):\n", + " if y_train_1[i] == 0:\n", + " X_train0.append(X_train_1[i])\n", + " y_train0.append(y_train_1[i])\n", + " else:\n", + " X_train1.append(X_train_1[i])\n", + " y_train1.append(y_train_1[i])\n", + " \n", + " X_train =[]\n", + " y_train = []\n", + " \n", + " if flag == \"fair\":\n", + " X_train = X_train0[:1000] + X_train1[:1000]\n", + " y_train = y_train0[:1000] + y_train1[:1000]\n", + " else:\n", + " X_train = X_train0[:10000] + X_train1\n", + " y_train = y_train0[:10000] + y_train1\n", + " \n", + " return np.asarray(X_train),np.asarray(y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "the dataset can be saved in two formats, the classic full dataset with approximately 25000 training data and approximately 6000 test data, or just the same size test and 200 training data." + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [], + "source": [ + "def save_data(X_train_1,y_train_1,flag=\"\",name=\"\"):\n", + " if flag == \"fair\":\n", + " X_train,y_train = balance_split(X_train_1,y_train_1,flag)\n", + " df_train = pd.concat([pd.DataFrame(X_train), pd.DataFrame(y_train)], axis=1)\n", + " df_train.to_csv(\"dataset/fair_\"+name+\".csv\", index=False)\n", + " else:\n", + " X_train,y_train = balance_split(X_train_1,y_train_1)\n", + " df_train = pd.concat([pd.DataFrame(X_train), pd.DataFrame(y_train)], axis=1)\n", + " df_train.to_csv(\"dataset/classic_\"+name+\".csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "apply the methos `save_data` for both cases fair and classical in train data" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "#classic data\n", + "save_data(X_train_1,y_train_1,flag=\"classic\",name=\"train\")\n", + "#fair data\n", + "save_data(X_train_1,y_train_1,flag=\"fair\",name=\"train\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "apply the methos `save_data` for both cases fair and classical in test data" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [], + "source": [ + "#classic data\n", + "save_data(X_test,y_test,flag=\"classic\",name=\"test\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we load the files and we correct the dimensions of these for the classic is **((20164, 16), (20164,))** and for the fair is **((2000, 16), (2000,))** for train set" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((20164, 16), (20164,))" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"dataset/classic_train.csv\")\n", + "X,y = data[data.columns[:16]].values, data[data.columns[16]].values\n", + "X.shape, y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((2000, 16), (2000,))" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"dataset/fair_train.csv\")\n", + "X,y = data[data.columns[:16]].values, data[data.columns[16]].values\n", + "X.shape, y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we load the files and we correct the dimensions of these for the classic and fair are **((6159, 16), (6159,))** for test set" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((6159, 16), (6159,))" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"dataset/classic_test.csv\")\n", + "X,y = data[data.columns[:16]].values, data[data.columns[16]].values\n", + "X.shape, y.shape" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": 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