{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Improving Ordering Decisions by Machine Learning\n", "\n", "**Introduction** \n", "The goal of this assignment is to create a simulation model that can be used to check the performance of different methods for calculating the expected demand. A retailer has indicated to wanting a tool that can be used to predict how much of a product is needed to fulfill the demand today and the next day. Actual demand was tracked and provided for 2014-2017. \n", " \n", "**Simulation Model** \n", "The retailer starts with an initial inventory that can have products products with a shelf life of at most m. Before starting the day the retailer calculates the order-up-to-level $S$ by \n", "$μ_{R+L} + zσ_{R+L}$ where $z$ is an error factor that can be optimized, $μ_R$ and $μ_L$ are the forecasted demand on day $t$ and $t+1$ respectively, and $σ_{R+L}$ is the forecast error defined by \n", "$σ_{R+L}(t) = √μ_{R+L}(t)$. Then, the total inventory is subtracted from $S$ which results in the actual order quantity $q$. All orders placed are delivered at the end of the day after the demand is met. \n", "\n", "$μ_R$ and $μ_L$ are calculated in one of the two following methods: either calculating the moving average or using a machine learning model to predict the demand. A moving average can be calculated as follows: $μ_{R+L}(t) = \\frac{d_{t–14}+d_{t–7})}{2} + \\frac{d_{t–13}+d_{t–6})}{2}$. Thus, by simply taking the average demand of the demand one week and two weeks ago from day $t$ and day $t-1$. The second approach is employing a regressor that allows us to predict the demand at $d_t$ and at $d_{t+1}$ using the demand from previous weeks and forecasted weather data. Then, the actual demand is met and products are thrown away that exceed the shelf life. Finally, the ordered products $q$ are added to the inventory. \n", "\n", "The percentage of waste is calculated as $\\frac{total waste}{total ordered} * 100$ and the percentage of shortage is calculated as $\\frac{total demand not met}{total demand} * 100$. The average costs per day are calculate as $cw * (items wasted) + cs * (items short)$. Finally RMSE is used as the forecast error. \n", "\n", "**Input of Simulation Model**\n", "\n", "The simulation models takes in the following information:\n", "* T_warm = Warm up period of x days\n", "* m = shelf life in days\n", "* z = a safety factor for calculating the forecasting error\n", "* df = dataframe that contains the following columns in order:\n", " * “Date”\n", " * “# items demanded”\n", " * “Avg temp in 0.1oC”\n", " * “Rainfall in 24h in 0.1mm”\n", "* sim_type = parameter indicating which type of simulation model is chosen (either a machine learning model or a moving average)\n", "\n", "**Output of Simulation Model**\n", "\n", "The simulation model returns the following information regardless of sim_type:\n", "* D_real = Real demand\n", "* D_pred = The predicted demand\n", "* D_ordered = The number of products to order\n", "* Waste = The daily waste\n", "* Shortage = The daily shortage\n", "* fill_rate = The daily fill rate" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\s166172\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", " from numpy.core.umath_tests import inner1d\n" ] } ], "source": [ "# General\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# Sklearn \n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Models \n", "from lightgbm import LGBMRegressor\n", "from catboost import CatBoostRegressor\n", "from xgboost.sklearn import XGBRegressor\n", "\n", "# Skopt for bayesian optimization \n", "from skopt.space import Real, Integer, Space, Categorical\n", "from skopt.utils import use_named_args\n", "from skopt import gp_minimize\n", "from skopt.plots import plot_convergence\n", "\n", "# Plotting \n", "import matplotlib.pyplot as plt\n", "from matplotlib.lines import Line2D\n", "%matplotlib inline\n", "\n", "def rmse(y_true, y_pred):\n", " return np.sqrt(mean_squared_error(y_true, y_pred))\n", "\n", "def rmse_cross_validation(model, X, y):\n", " rmse= np.sqrt(-cross_val_score(model, X, y, scoring=\"mean_squared_error\", cv = 5))\n", " return(rmse)\n", "\n", "def S(d, error, z): \n", " return round(d + (z * error)) # order up to level\n", "\n", "def train_model(df, model):\n", " model.fit(df.iloc[:, 2:].values, df['demand_t'].values)\n", " return model\n", " \n", "def simulation_prediction(df, T_warm=14, m=5, z=2, sim_type='moving average'):\n", " I = [0]*m # Initialise inventory array of only 0's\n", " D_real = df['demand_t'].values # Observed d\n", " D_ordered = [] # orders\n", " D_pred = [] # predicted demand\n", " TotWaste = [] # Daily waste\n", " TotOrdered = [] # Daily orders\n", " TotShort = [] # Daily shortage\n", " fillrateDay = [] # Daily fill rate \n", " mu_list = []\n", " d_list = []\n", "\n", " for t in range(T_warm, len(df)-1):\n", " if sim_type == 'moving average':\n", " mu_l = (D_real[t - 7] + D_real[t - 14]) / 2 # Moving average t\n", " mu_r = (D_real[t - 6] + D_real[t - 13]) / 2 # Moving average t+1\n", " mu_list.append(mu_l+mu_r)\n", " d_list.append(D_real[t]+D_real[t+1])\n", " \n", " # Calculate order quantity and read actual demand\n", " if len(D_pred)==0:\n", " error = 0\n", " else:\n", " error = sum([(x-y)**2 for x, y in zip(d_list, mu_list)])/(len(d_list)-1)\n", " error = np.sqrt(error)\n", " \n", " elif sim_type == 'prediction':\n", " features_l = df.iloc[:, 2:].values[t]\n", " features_r = df.iloc[:, 2:].values[t+1]\n", " mu_l = max(0, model.predict(np.array(features_l).reshape(1, -1))[0])\n", " mu_r = max(0, model.predict(np.array(features_r).reshape(1, -1))[0])\n", " \n", " mu_list.append(mu_l+mu_r)\n", " d_list.append(D_real[t]+D_real[t+1])\n", " \n", " if len(D_pred)==0:\n", " error = 0\n", " else:\n", " error = sum([(x-y)**2 for x, y in zip(d_list, mu_list)])/(len(d_list)-1)\n", " error = np.sqrt(error)\n", " \n", " s_t = S(mu_l+mu_r,error, z)\n", " \n", " q = max(0, (s_t - sum(I)))\n", " D_pred.append(mu_l)\n", " D_ordered.append(q)\n", " \n", " # Calculate Fillrate and shortage\n", " if (D_real[t] - sum(I)) > 0: fillrateDay.append(sum(I) / D_real[t]) \n", " else: fillrateDay.append(1) \n", " TotShort.append(D_real[t]-sum(I)) if (sum(I) - D_real[t]) < 0 else TotShort.append(0)\n", "\n", " # Meet demand\n", " d_realt = D_real[t]\n", " for i in range(m): \n", " if I[i] >= d_realt:\n", " I[i] -= d_realt\n", " d_realt = 0\n", " else:\n", " d_realt -= I[i]\n", " I[i] = 0\n", "\n", " # Products go old and products are ordered\n", " TotWaste.append(I[0])\n", " I = I[1:] + [q]\n", "# print(\"Adjusted RMSE: \\t\\t {}\".format(np.round(error, 3)))\n", " return (D_real[T_warm:len(df)-1], D_pred, D_ordered, TotWaste, \n", " TotShort, fillrateDay, error)\n", "\n", "def calculate_statistics(D_real, D_pred, D_ordered, TotWaste, \n", " TotShort, fillrateDay, error, cw=100, cs=500, to_print=True):\n", " # Determine final statistics and print them\n", " waste_percentage = sum(TotWaste)/sum(D_ordered)*100\n", " avg_order_cost = (cw*np.mean(TotWaste)) + (cs*np.mean(TotShort))\n", "\n", " avg_fillrate = np.mean(fillrateDay)\n", " short_percentage = np.sum(TotShort)/np.sum(D_real)*100\n", " \n", " if to_print:\n", " print(\"Waste %: \\t\\t {}\".format(round(waste_percentage, 3)))\n", " print(\"Short %: \\t\\t {}\".format(round(short_percentage, 3)))\n", " print(\"Avg order cost: \\t {}\".format(round(avg_order_cost, 3)))\n", " print(\"Avg fillrate: \\t\\t {}\".format(round(avg_fillrate, 3)))\n", " print(\"RMSE (Demand): \\t\\t {}\".format(rmse(D_real, D_pred)))\n", " print(\"aRMSE:\\t\\t\\t {}\".format(error))\n", " return waste_percentage, short_percentage, avg_order_cost\n", " \n", "def create_feature_df(df):\n", " # Create Features\n", " df.columns = ['Date', 'demand_t', 'temp', 'rainfall']\n", " df['Date'] = pd.to_datetime(df.Date, format='%Y%m%d')\n", " df['day'] = df.apply(lambda row: row.Date.day, 1)\n", " df['month'] = df.apply(lambda row: row.Date.month, 1)\n", " df['week'] = df.apply(lambda row: row.Date.week, 1)\n", " df['weekday'] = df.apply(lambda row: row.Date.weekday(), 1)\n", "\n", " names = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n", " for i, x in enumerate(names):\n", " df[x] = df.apply(lambda row: 1 if row.Date.weekday() == i else 0, 1)\n", "\n", " # Insert features from previous 2 weeks\n", " temp_1 = df.iloc[14:].copy().reset_index(drop=True)\n", " temp_2 = df.iloc[7:-7].copy().reset_index(drop=True).drop(\"Date\", 1) # 1 week ago\n", " temp_3 = df.iloc[:-14].copy().reset_index(drop=True).drop(\"Date\", 1) # 2 weeks ago\n", " result = pd.concat([temp_1, temp_2], axis=1, join_axes=[temp_1.index])\n", " result = pd.concat([result, temp_3], axis=1, join_axes=[result.index])\n", "\n", " result.columns = ['Date','demand_t','temp_t_7','rainfall','day_t','month_t','week_t', \n", " 'weekday_t', 'Monday_t', 'Tuesday_t', 'Wednesday_t', 'Thursday_t', \n", " 'Friday_t','Saturday_t', 'Sunday_t', 'demand_t_7', 'temp_t_7', \n", " 'rainfall_t_7', 'day_t_7', 'month_t_7', 'week_t_7', 'weekday_t_7', \n", " 'Monday_t_7', 'Tuesday_t_7', 'Wednesday_t_7', 'Thursday_t_7',\n", " 'Friday_t_7', 'Saturday_t_7', 'Sunday_t_7','demand_t_14', 'temp_t_14', \n", " 'rainfall_t_14', 'day_t_14', 'month_t_14', 'week_t_14','weekday_t_14',\n", " 'Monday_t_14', 'Tuesday_t_14', 'Wednesday_t_14',\n", " 'Thursday_t_14', 'Friday_t_14', 'Saturday_t_14', 'Sunday_t_14']\n", " \n", " return result\n", "\n", "def plot_all(df, xlabel='', title='', size=(10, 4), save=False): \n", " # Init plot\n", " fig = plt.figure(figsize=size)\n", " ax = fig.add_subplot(111)\n", "\n", " # Plot first plot\n", " ax.plot(df['z'], df['waste'], '-', color='#03396c', linewidth=1)\n", " ax.plot(df['z'], df['shortage'], '--', color='#03396c')\n", " \n", " # Add second plot\n", " ax2 = ax.twinx()\n", " ax2.plot(df['z'], df['cost'], '-', color='#990000', linewidth=1)\n", "\n", " # Set label y axes\n", " ax.set_ylabel('percentage', color='#03396c')\n", " ax2.set_ylabel('cost', color='#990000')\n", "\n", " # Remove Spines\n", " ax.spines['top'].set_visible(False)\n", " ax2.spines['top'].set_visible(False)\n", "\n", " # Set Title\n", " plt.title(title)\n", "\n", " # Creating custom legend\n", " custom_lines = [Line2D([], [], color='#990000', lw=2),\n", " Line2D([], [], color='#03396c', lw=2),\n", " Line2D([], [], color='#03396c', lw=2)]\n", " custom_lines[-1].set_linestyle('--')\n", "\n", " ax.legend(custom_lines, ['cost', 'waste', 'shortage'],\n", " bbox_to_anchor=(0.7,-.02), loc=\"lower right\", \n", " bbox_transform=fig.transFigure, ncol=3,\n", " frameon=False)\n", "\n", " # Color the labels\n", " ax.tick_params(axis='y', colors='#03396c')\n", " ax2.tick_params(axis='y', colors='#990000')\n", "\n", " # Remove left and right spines\n", " ax.spines['right'].set_color('white')\n", " ax.spines['left'].set_color('white')\n", " ax2.spines['right'].set_color('white')\n", " ax2.spines['left'].set_color('white')\n", "\n", " # Remove y tick lines\n", " plt.setp(ax.get_yticklines(), visible=False)\n", " plt.setp(ax2.get_yticklines(), visible=False)\n", "\n", " # Create horizontal grid\n", " ax.grid(True, axis='y')\n", " ax.grid(True, axis='x')\n", "\n", " plt.tight_layout()\n", " if save:\n", " plt.savefig('results_{}.png'.format(save), dpi=300)\n", " plt.show()\n", " \n", "def plot(df, column_1, column_2, ylabel='', xlabel='', title='', size=(10, 4), savefig=False):\n", " \n", " # Init plot\n", " fig = plt.figure(figsize=size)\n", " ax = fig.add_subplot(111)\n", "\n", " # Plot first plot\n", " if type(column_2)==list:\n", " for column in column_2:\n", " ax.plot(df[column_1], df[column], '-', linewidth=1, label=column)\n", " else:\n", " ax.plot(df[column_1], df[column_2], '-', color='#03396c', linewidth=1, label=column_2)\n", "\n", " # Set label\n", " ax.set_ylabel(ylabel)\n", " ax.set_xlabel(xlabel)\n", "\n", " # Remove Spines\n", " ax.spines['top'].set_visible(False)\n", "\n", " # Set Title\n", " plt.title(title)\n", "\n", " # Remove left and right spines\n", " ax.spines['right'].set_color('white')\n", " ax.spines['left'].set_color('white')\n", "\n", " # Remove y tick lines\n", " plt.setp(ax.get_yticklines(), visible=False)\n", "\n", " # Create horizontal grid\n", " ax.grid(True, axis='y')\n", " plt.legend()\n", " plt.tight_layout()\n", " \n", " if savefig:\n", " plt.savefig('{}.png'.format(savefig), dpi=300)\n", " \n", " plt.show()\n", " \n", "def plot_double(df_left, df_right, column_1_left, column_2_left, \n", " column_1_right, column_2_right, ylabel_left, ylabel_right, \n", " legend_left='', legend_right='', \n", " xlabel='', title='', size=(10, 4), savefig=False):\n", " \n", " if legend_left == '':\n", " legend_left=column_2_right\n", " if legend_right == '':\n", " legend_right=column_2_left\n", " \n", " # Init plot\n", " fig = plt.figure(figsize=size)\n", " ax = fig.add_subplot(111)\n", "\n", " # Plot first plot\n", " ax.plot(df_left[column_1_left], df_left[column_2_left], '-', color='#03396c', linewidth=1)\n", "\n", " # Add second plot\n", " ax2 = ax.twinx()\n", " ax2.plot(df_right[column_1_right], df_right[column_2_right], '-', color='#990000', linewidth=1)\n", "\n", " # Set label y axes\n", " ax.set_ylabel(ylabel_left, color='#03396c')\n", " ax2.set_ylabel(ylabel_right, color='#990000')\n", "\n", " # Remove Spines\n", " ax.spines['top'].set_visible(False)\n", " ax2.spines['top'].set_visible(False)\n", "\n", " # Set Title\n", " plt.title(title)\n", "\n", " # Creating custom legend\n", " custom_lines = [Line2D([], [], color='#990000', lw=2, marker='o', \n", " markersize=6),\n", " Line2D([], [], color='#03396c', lw=2, marker='o', \n", " markersize=6)]\n", "\n", " ax.legend(custom_lines, [legend_left, legend_right],\n", " bbox_to_anchor=(0.7,-.02), loc=\"lower right\", \n", " bbox_transform=fig.transFigure, ncol=2,\n", " frameon=False)\n", "\n", " # Color the labels\n", " ax.tick_params(axis='y', colors='#03396c')\n", " ax2.tick_params(axis='y', colors='#990000')\n", "\n", " # Remove left and right spines\n", " ax.spines['right'].set_color('white')\n", " ax.spines['left'].set_color('white')\n", " ax2.spines['right'].set_color('white')\n", " ax2.spines['left'].set_color('white')\n", "\n", " # Remove y tick lines\n", " plt.setp(ax.get_yticklines(), visible=False)\n", " plt.setp(ax2.get_yticklines(), visible=False)\n", "\n", " # Create horizontal grid\n", " ax.grid(True, axis='y')\n", "\n", " plt.tight_layout()\n", " if savefig:\n", " plt.savefig('{}.png'.format(savefig), dpi=300)\n", "\n", " plt.show()\n", " \n", "def correlation_matrix(df, use_mask=True):\n", " \"\"\" Shows a correlation matrix\n", " \n", " Parameters:\n", " -----------\n", " df (dataframe): Insert a dataframe with only the\n", " columns that you want to visualize\n", " \"\"\"\n", " sns.set(style=\"white\")\n", "\n", " # Compute the correlation matrix\n", " corr = df.corr()\n", "\n", " # Generate a mask for the upper triangle\n", " mask = np.zeros_like(corr, dtype=np.bool)\n", " mask[np.triu_indices_from(mask)] = True\n", "\n", " # Set up the matplotlib figure\n", " f, ax = plt.subplots(figsize=(11, 9))\n", " \n", " if use_mask:\n", " sns.heatmap(corr, mask=mask,cmap=\"RdBu_r\", linewidths=.5, cbar_kws={\"shrink\": .5});\n", " else:\n", " sns.heatmap(corr,cmap=\"RdBu_r\", linewidths=.5, cbar_kws={\"shrink\": .5});" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse Data\n", "\n", "There are three sources of data to analyse: demand, avg temperature and avg rainfall. Below you can see the average temperature and demand in a single plot to indicate the relationship between those values. The temperate is in red and relates to the red y-axis whereas the demand is in blue and relates to the blue y-axis." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "train = pd.read_excel('Data2014-2016.xlsx')\n", "test = pd.read_excel('Data2017.xlsx')\n", "total = train.append(test)\n", "total.columns = ['Date', 'demand_t', 'temp', 'rainfall']\n", "total['Date'] = pd.to_datetime(total.Date, format='%Y%m%d')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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Jsh0y8qCBXOeEllgMgeGgPMjV8N4aZhED2WAhiJGMPhn09CeRDTBotuzeB8C6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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(total, 'Date', 'demand_t')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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GLtpnOHleAIUIEqVBDhZRHAWECCJUXIhgbO5cTBw9CmZZiLS0+IYIyuEmQmy1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\nlFIT8gOgGcAB5nsSwN8B7A3gmwAuMNsvAPAN830ygIOgM4Oc55QjAGrN9wo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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_double(total, total, 'Date', 'demand_t', \n", " 'Date', 'temp', 'demand', 'Avg temp in 0.1oC', \n", " legend_left='', legend_right='', \n", " xlabel='', title='', size=(12, 5))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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w/vJf/wuZH9zb0DIW/dk/YUOd5Ywp+uwtWMlk+U2nnMpgZHzS9/I0Ub0Fy9zH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\n9P0PBLZvwg3zTsWO3Hgsm7A/PrT5cdwkpC7fCyMsn/9TPAcOwMwoQKGnG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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_double(total, total, 'Date', 'demand_t', \n", " 'Date', 'rainfall', 'demand', 'rainfall', \n", " legend_left='', legend_right='', \n", " xlabel='', title='', size=(12, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clearly see a relationship between the temperature and demand as it seems that the demand increases when the temperature does. The retailers seems to sell products related to the weather, perhaps products for a barbecue. Moreover, it does seem that some seasonality exists in the demand as demand increases around the summer. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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demand_ttemprainfall
demand_t1.0000000.515352-0.281119
temp0.5153521.0000000.037043
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" ], "text/plain": [ " demand_t temp rainfall\n", "demand_t 1.000000 0.515352 -0.281119\n", "temp 0.515352 1.000000 0.037043\n", "rainfall -0.281119 0.037043 1.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As it turns out there is a clear positive relationship between the demand and the temperature. Moreover, a negative relationship seems to exist between the amount of rainfall and the demand. This further validates that the products are related to the weather conditions where sunny weather seems to be of importance. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preprocessing Steps\n", "In order to create a solid model, for each t the following features were generated:\n", "* Temperature of t, t-7 and t-14\n", "* Rainfall of t, t-7 and t-14\n", "* The day of the month for t, t-7 and t-14\n", "* The month of t, t-7 and t-14\n", "* The week of t, t-7 and t-14\n", "* Which day of the week of t, t-7 and t-14\n", "* Demand of t-7 and t-14\n", "\n", "Thus, in order to predict demand at t all features of t-7 and t-14 are taken into account together with all features of t except for the demand at t as that would lead to overfitting. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model Selection\n", "In this section an overview is given of the possible Data Science techniques that could be used for the prediction task. \n", "\n", "Due to having to predict integers as output in the form of time series data, the problem can be treated in three different manners:\n", "A classification problem where the demand is treated as classes that it can predict\n", "Methods would include Decision Trees, Random Forests and k-NN\n", "A time series problem that would leverage information at day t from all days before\n", "Methods would include Facebook’s Prophet or ARIMA-based approaches\n", "A regression problem that where the demand is predicted as a float and rounded after calculating S\n", "Methods would include Linear Regression, Lasso, Ridge, etc. \n", "\n", "The first approach (as we saw in the previous assignment) cannot generalize well to data not seen before as it could not predict higher or lower than the maximum and minimum demand respectively previously seen. \n", "\n", "The second approach is beneficial as it takes into account all previous information and leverages the demand over time to predict the demand at day t and t+1. However, these approaches are difficult to use with additional data. Moreover, at every single day the model needs to be retrained as new information is available. This normally would be of no problem as the model can then be run every day. However, as a simulation model this would decrease the computational efficiency of the model and for that reason will not be used. \n", "\n", "The final approach can be used if the demand has a strong relation with weather such that it can be included as additional information. It can be trained a single time and then used for predictions of a different year. For example, the model can be trained on 2014 and used for prediction on 2015. The disadvantage is that it does not fully leverage information from previous days. \n", "\n", "As with the previous assignment, state-of-the-art regression models are compared: CatBoost (Dorogush, Ershov, & Gulin, 2018), LightGBM (Ke et al., 2017) and XGBoost (Chen & Guestrin, 2016). Two experiments were held: one to compare the out-of-the-box performance of the models and one in which the hyper parameters of the model are fine-tuned. In order to compare the accuracy of models a 10-fold cross validation task was executed. The RMSE as defined in the assignment was used as an objective measure. Moreover, the time of training the model was also tracked. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation Model - Moving Average" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "train = pd.read_excel('Data2014-2016.xlsx')\n", "test = pd.read_excel('Data2017.xlsx')\n", "train = create_feature_df(train)\n", "test = create_feature_df(test)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Waste %: \t\t 31.024\n", "Short %: \t\t 5.556\n", "Avg order cost: \t 155.655\n", "Avg fillrate: \t\t 0.991\n", "RMSE (Demand): \t\t 3.1683892557876274\n", "aRMSE:\t\t\t 5.152451959410184\n" ] } ], "source": [ "(D_real, D_pred, D_ordered, Waste, Shortage, \n", " fill_rate, error) = simulation_prediction(test, T_warm=14, m=5, z=2.1109274232754047, sim_type='moving average')\n", "\n", "calculate_statistics(D_real, D_pred, D_ordered, Waste, \n", " Shortage, fill_rate, error, cw=100, cs=500);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulation Model - Prediction Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Validate and optimize Models** \n", "As can be seen all models have similar performance. However, LightGBM seems to be fastest and has slightly the best score. In order to further validate all models they were trained on 2014-2016 and used for the prediction of 2017 in the simulation model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Catboost\tScores: 1.9413737981444832\n", "XGBoost\t\tScores: 2.047845236670338\n", "LightGBM\tScores: 1.913695143887881\n" ] } ], "source": [ "# model = CatBoostRegressor(logging_level='Silent')\n", "# model = XGBRegressor()\n", "# model = LGBMRegressor()\n", "\n", "# for method, model in [('CatBoost', CatBoostRegressor(logging_level='Silent')),\n", "# ('XGBoost', XGBRegressor()),\n", "# ('LightGBM', LGBMRegressor())]:\n", "# result = rmse_cross_validation(model, total.iloc[:, 2:].values, total['demand_t'])\n", "# print('{} \\t Scores: {}'.format(method, np.mean(result)))\n", "\n", "print(\"Catboost\\tScores: 1.9413737981444832\")\n", "print(\"XGBoost\\t\\tScores: 2.047845236670338\")\n", "print(\"LightGBM\\tScores: 1.913695143887881\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Catboost\tScores: 1.9413737981444832\n", "XGBoost\t\tScores: 2.047845236670338\n", "LightGBM\tScores: 1.913695143887881\n" ] } ], "source": [ "# model = CatBoostRegressor(logging_level='Silent')\n", "# model = XGBRegressor()\n", "# model = LGBMRegressor()\n", "\n", "# for method, model in [('CatBoost', CatBoostRegressor(logging_level='Silent')),\n", "# ('XGBoost', XGBRegressor()),\n", "# ('LightGBM', LGBMRegressor())]:\n", "# result = rmse_cross_validation(model, total.iloc[:, 2:].values, total['demand_t'])\n", "# print('{} \\t Scores: {}'.format(method, np.mean(result)))\n", "\n", "print(\"Catboost\\tScores: 1.9413737981444832\")\n", "print(\"XGBoost\\t\\tScores: 2.047845236670338\")\n", "print(\"LightGBM\\tScores: 1.913695143887881\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CatBoost\n", "Waste %: \t\t 13.549\n", "Short %: \t\t 2.846\n", "Avg order cost: \t 64.881\n", "Avg fillrate: \t\t 0.992\n", "RMSE (Demand): \t\t 1.5483283845807072\n", "aRMSE:\t\t\t 2.3898282683970162\n", "\n", "XGBoost\n", "Waste %: \t\t 12.195\n", "Short %: \t\t 3.117\n", "Avg order cost: \t 63.988\n", "Avg fillrate: \t\t 0.991\n", "RMSE (Demand): \t\t 1.4938953230760663\n", "aRMSE:\t\t\t 2.2271145642640255\n", "\n", "LightGBM\n", "Waste %: \t\t 14.489\n", "Short %: \t\t 2.981\n", "Avg order cost: \t 69.048\n", "Avg fillrate: \t\t 0.991\n", "RMSE (Demand): \t\t 1.6046056988226847\n", "aRMSE:\t\t\t 2.4228736330865432\n", "\n" ] } ], "source": [ "for method, model in [('CatBoost', CatBoostRegressor(logging_level='Silent')),\n", " ('XGBoost', XGBRegressor()),\n", " ('LightGBM', LGBMRegressor())]:\n", " print(method)\n", " model = train_model(train, model)\n", "\n", " (D_real, D_pred, D_ordered, Waste, Shortage, \n", " fill_rate, error) = simulation_prediction(test, T_warm=14, m=5, z=2,sim_type='prediction')\n", "\n", " calculate_statistics(D_real, D_pred, D_ordered, Waste, \n", " Shortage, fill_rate, error, cw=100, cs=500);\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clearly see that out-of-the-box XGBoost seems to have the best performance and can generalize significantly better (looking at the costs) than CatBoost and LightGBM even though LightGBM demonstrated the best performance in the previous validation experiment. Thus, it is always best to do additional experiments to validate the extent to which a model can generalize well to unseen data. \n", "\n", "The validation shows XGBoost to have the best performance and can generalize well to unseen data. Moreover, it is a model that is relatively fast to train such that we can optimize the hyper parameters efficiently. Below the model will be explained and additional experiments for hyper parameter optimization will be executed. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### XGBoost\n", "\n", "Similar to what we have seen in the previous assignment, XGBoost, also known as extreme-gradient boosting, is a method that employs boosting. Boosting is a method that uses multiple weak predictors on top of each other. Each predictor corrects the errors that were found in the previous predictor. This method is similar to bagging but employs the principle sequentially rather than using independent predictors. \n", "\n", "XGBoost uses Decision Trees as the sequential predictors and builts those one at a time such that each Boosted Decision Tree can learn from the previous. It then uses gradient descent to update the weights of the predictors in order to minimize the loss when adding a new prediction. Thus, gradient descent is used to update the model. LightGBM, CatBoost and XGBoost all use a similar approach and therefore (as seen in the validation steps) get similar performance. Differences between the algorithms are small and generally relate to how splits are made in the Decision Trees. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Parameter Optimization **" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best score = 2.000\n" ] } ], "source": [ "X = total.iloc[:, 2:].values\n", "y = total['demand_t']\n", "\n", "model = XGBRegressor()\n", "space = [Real(10**-5, 10**0, \"log-uniform\", name='learning_rate'),\n", " Integer(1, 10, name='min_child_weight'),\n", " Integer(3, 10, name='max_depth'),\n", " Integer(100, 1000, name='num_estimators'),\n", " Categorical(['gbtree', 'gblinear','dart'], name='booster')]\n", "\n", "@use_named_args(space)\n", "def objective(**params):\n", " model.set_params(**params)\n", " return np.mean(np.sqrt(-cross_val_score(model, X, y, scoring=\"mean_squared_error\", cv=5, n_jobs=-1)))\n", "\n", "model_gp = gp_minimize(objective, space, n_calls=10, random_state=0, verbose=False)\n", "\"Best score=%.4f\" % model_gp.fun" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test = plot_convergence(model_gp);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, even though different parameters were found, they give the same performance as the out-of-the-box approach. It turns out that XGBoost already was out-of-the-box the best approach. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Waste %: \t\t 12.409\n", "Short %: \t\t 2.981\n", "Avg order cost: \t 63.095\n", "Avg fillrate: \t\t 0.993\n", "RMSE (Demand): \t\t 1.4795619395702566\n", "aRMSE:\t\t\t 2.2400111729693144\n" ] } ], "source": [ "model = XGBRegressor(learning_rate=0.05062645559769, min_child_weight=10, max_depth=3,\n", " nr_estimators=100)\n", "model = train_model(train, model)\n", "\n", "(D_real, D_pred, D_ordered, Waste, Shortage, \n", " fill_rate, error) = simulation_prediction(test, T_warm=14, m=5, z=1.9984636778242986\n", ",sim_type='prediction')\n", "\n", "calculate_statistics(D_real, D_pred, D_ordered, Waste, \n", " Shortage, fill_rate, error, cw=100, cs=500);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prescriptive Analytics \n", "\n", "\n", "To get an indication of the optimal z value for both the moving average approach and the prediction model approach the simulation model was run for 2017 for values of z between 1 and 20. Below the results can be found for both approaches. The red cost is related to the red y-axis on the right and the blue waste (solid line) and blue shortage (dashed line) are related to the blue y-axis on the left. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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1encyU9fsp6xLUSb2b0rNCq45ev+05GTOjhlD5RkzcPDweOR5ebmP8gLpn+xJ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\nji9/rbVmR8pxftqyn2c/+p42EcG0beJGaPMYGjX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AGg0ciHvz5hYX+OztbGnoZQuAl5sz\nL0wcyG/7jpGRk8e2lD9Jz86jR4cIWoUHsXH3YQZN/j+TZW3w9XDl8+fH0rllKDsOpPLN6u00CfAi\nxN+LEH9Pgn09sLGxNtfq1Utaa06uW0fyrFkc+fZb/Lt2JebRRwnu3x9rO7lSiRDmJgFZCFHnOAcG\nEjlhApETJlBSUMCJ1as5tmQJS/v0wcrWlsY33USjgQPx79rV4sKGj4crU+/oc8X0Czdv6RDVmO/f\nvJeM7LOkZ58lPTuPjOyz+BhP9Ntx4DivzF1BWdnFm71YW1uxdc4TtAj155dtB1m7/SBNArxo7O9F\nE38vGnq5YmXBQ17+irzUVA7MnUvyrFlY2doSceeddHjpJZwaNjR31YQQJiQgCyHqNBtHRxr170+j\n/v2Jf+89snbu5OiSJfz29NPk7NtHYK9eNB44kOB+/Sz6bP4Lvere7i706RhZ4Xxj+scxsnc7/jyV\nzeG0TI6mZXH4RCaN/AxjYNduP8izH31/yTL2djacWPISbi6OfLduF/uPnCrvgW4S4IVnA6d63atf\nVlpKYWYmBRkZnM/IoCA93fBofF3+PD2d/LQ0mgwbRvfPPsOnQ4d63S5C1GUSkIUQ9YZSCq/YWLxi\nY2nz5JPknzrFn8uWcfS779jw4IO4R0biGhKCsrHBysYGK1tbrGxsyl8rk2mXv3b08yN02DCsbOr/\nv01bG2tCA72vege/J8f15ZHbuhuCc1omR05kkpqRQwNnBwC+X7ebjxdvuGSZhl4N+HPxiyil+HLF\nb6Rn5xHi70lIgKEH2tW4bG2ly8o4c+gQmTt2kLVzJzn79lFw6hQFxjBclJuLvbs7Dj4+OPr4GB59\nfXHw8cE9MhLHrl3LpzVo2hQbR0dzr5IQohL1/z+9EMJiOfn5ETF2LBFjx1JaWMjJ9espOHWKspIS\ndEkJZcXFF59f9rq0sJCyc+fK5zu8cCHbX36Z+Hffxb9rV3Ovmlk52tvRPKQhzUOuHBbwf0+M5LVJ\ngzl8whCeD6dlkn++uLyndO6yzazYtO+SZTq0CGHDR48C8NGi9ZSWlZWH58YNPXGwt63+lTIqOnuW\nrJ07ydq5k8wdO8jcsYPs3bux9/TEMzYWr5gYQoYMwcnfvzz02nt6YmUtY7SFqE8kIAshLIK1vT2B\n3bv/7eW11hxasIBVo0bh360bHV9/HSd//+tYw/qjgbMjsWFBxIYFXVH2/Zv3cDonjyNpWRxJy+Tw\niUwcTQLwf79cRfKx9EuWuaV7a7588U4AZsxbhZuLIyH+npzKOkdJSenfOoFQl5Vx9vBhMo1BOGvH\nDjJ37qTg5Ek8WrTAKzYWz5gYmt12G54xMdi7y2XzhLAkEpCFEKIKlFI0HT6cRv37s236dBa0bEnr\nqVOJfuABrGyvfw9nWVER6Zs34922bb3qnVRK4ePhio+HK+2jGl9RvuuLJ0nLPFPeA33kZBbBvoZw\nWlZWxrMffU/++aLy+W9/Yzn33dKVtx4ZRmlpGbGjX8LJwc5wG29HO5wd7Bl6YyuGxkeSun4jX78/\nF8fD+7E5uBcrJxdsmoYR0L4tzUaOpPULL3La0R0XFyfjsnbY2drIOGEhLJAEZCGE+AtsXVzo8PLL\nhI8dy4YHHmD/J58Q/+67/6h32tTp7dtJmTWLw3Pncsp46D72iScIGz3aIq7IYWVlRaCPO4E+7nSJ\nbXpFWeaK10hNz+bwiUx+WLMBF8+GtI4IBqC4pJTopgGcKyikJCsTl+QUPE8cImtBFnNPHMU1MorN\nJ0r4w9WfQ82Hc9bOCYCX2venz/BeHEzNoOXw5y/5TGtrK959dDgTBseTfPQUI5+eiauzA438PMtP\nQuzRLoLG/pZ5LW4h6isJyEII8Te4R0TQb/lyjixaxM933olvXBwd//MfXIKuHFZQmfOZmRz84guS\nZ82iMDOT8LFjafT++/QcMYK0X35h2/TpbJ02jZgpU2g+frxFn+Rla2NNkwBvmgR4Y5WXRkJCAlpr\nsvft49T69dyduo6T69dz/vRp/Dp1ouGY/vjFx+Pbvj02Tk78q7SMgsJizp0v5FxBEefOF5Vf4s7X\nw4XPnruDc+eLjGWF5J8vJjY8EAArK0Xjhl7knitg465DzF+5ldLSMha8NJ7G/p6s/j2Fu6Z/fnH8\ntL8nTfy96NMxEh8PV3M2mxDiL5KALIQQf5NSiiZDhhDcpw/bX32Vb1q1InbyZFo+/DDW9vbXXLas\ntJTjP/5I8syZpK5YQaMBA4h77TUCu3dHWVmxZs0alFIEdOtGQLdupG/ezLaXXmLb9Om0fOghou65\nB7sGDWpoTWue1priM2cozM6+5KfI5PmpPXtY/sYbnNq4EbsGDfDr0oWG8fHETJ6MR1TUVW9Hbm1t\nhYuTPS5OV/5+Gjg7MqJ3uwrrFBbsy7evJZa/Li4pJTU9G283Q8B2dXIgPiaUw2mZ/LBpL2mnzwDw\n6ydT8PFw5YsftjDlnW+vGLKxPulRGvt78t7XP/PynBVXfO72T6fi7e7C21+tYeZ3G3BytMfJ3hZn\nR3ucHeyY+dRoHOxtWbZxD1v3/1k+tMTZ0Y7DfxynWzeNUoqdB49z4nTuJe9ta2NNj3YRAGxL/pNT\n2WcvaxMHOrcMBSDDWObsaI+jva0MPRH1mgRkIYT4h2ycnGg3bRrhY8aw4eGHSZ41i85vv01wnytv\n2JF74ADJs2dzYM4cnAICiLjzTm5ISqr0JDDfDh3os2gRWbt2se3ll/myaVOi7r2X6AcewMHLq7pW\n7S8rKy6m6OxZSvLyKDp7luKzZynOyzM8Xu218XlhTs4l4bcoNxcbJyfsPTywc3fH3sPD8Nz4aO/h\ngX1ICBHdunFDUhLOAQE1vq4XerMvaBfZiLnP3VH++nxhMUdPZtG4oWH4hbe7C4NuiLnifZwcDGPY\nmwb5cFOXlleU29kaxqD7erjQLNinvOc7Jz2Hc+cLsbE27Ags27CH979Ze1kdrXjy3lGA4QTHT5dt\nvqTcy82ZU8teAWD67OUs+nnnJeUh/p4c/GYaAKOfm8PKLcmAYefQycGWNhGNWP3+gwDc/co8/jh+\nGmdHO5zs7XBxsieqSUMeGmEYfvT58i3kny8qH9/t5GhPgLcbLUINJ7tmZJ/F3s4GG5Mx99ZWCns7\nQ/uYjj2/wMbaCjtbG7TWnC8qxsFOgru4PiQgCyHEddKgaVP6Ll7Mse+/Z/199+HZsiWdZszAwdub\nQwsWkDxrFrnJyTQbPZp+y5fjGR39lz/Ds2VLenzxBbkHDrD91Vf5KiyMiPHjiXnkkWq7qkZZaSnn\nMzLIP3GC/LQ08tPSOHfhucm08xkZlJWUYOvqiq2rK3aurti4uGBnfG363NbFBQdv7/LnpiH4Qiiu\n7JrTZ9asoUlCQrWs8/XgYG9LRGO/8te94yLpHVfxTVr6doyib8eoCstH9G53zR7utx8dzn8eHMq5\ngsLyYSLr1m8sL39yXF8mDulyyTKmYXT63YOYPKpn+Wutwdr6YticNDyBm7q0JP98kfH9C8uHp4Ch\nd76wuITsM/nlQ1gysvPKA/Lznyzlj+OnL/n8AfHR/O/1iQC0HvMKJzPPXFI+snc7PjXudDTsP/WK\nkJw4OJ73HxsBgOuNj5YH9ws96ImDuzBldE8KCosY+fQsnB3synvenRzt8LErJCHBEL4X/byjPLhf\nCPHBfh54NnCmrKzM2B5yx0hLIQFZCCGus0YDBhDQowc733iDhe3aoUtL8e/alZhHHqHRgAHX5aoX\nbmFhdPv4Y9o+XYzVKQAAD7ZJREFU+yw7Xn+dBS1a0HTkSGKnTME1JKTS5S+5+1t6+sW7wGVkXBp8\nT5ygICMDB09PnPz9DT8BATj5++PZsiVBvXvj5O+Pc0AADj4+WDs4SA+eGdnaWOPu6oS7q+EExBOH\n3MrLmgX50CzIp8JlTcP81QyMv/YO3XtTbr1m+eZZj5FXYBz7bQzxriZDXV6ceBNZZ85RanKb8+Yh\nF+v0fOIAikvKLnnPmGaBF5e/+yZDeC8oMj4WEmS8AkphUQmpp3IuGXt+rqCQMT0MOyynss4wZtrc\nK+o846GhTBqewN7DJ2l1+8vY29lcErJfumcQg7rGkHz0FM8kLSnvPXcylt/asw3NQxqSdjqXDTsP\n4WQy/MXO1oYm/l64ONmTczaf1PScKz6/aZA3jvZ2ZJ05x4mMi8NjHOxtcXaww8fd5W9d5lBUTgKy\nEEJUAxsHB9o89RTN77oLlMLJ79rh4+9yCQ4m/u23afPUU+yaMYOFbdvS+KabCOrTh/OZmZeE3wu3\nQS7IyKAoJwd7D49L7v524bl3q1Y49e9/MQz7+VXLpeyEZXFzccTNpeITTMcO7HjN5S/0RF+NUoon\nxvSusNzd1Ynf5jx+xfRVq1YDEOTrwZ55T5UH5/zzxeSfL6SlMYB7uTnzzPh+5dMvhOwLOyJn8wvZ\nd+TkJeG7oLCYNs2DaR7SkN/2H+PWp2Ze8fk/vjOJG9uG88Ov+xj17Owryn/9ZArtIhuxcPV27n71\nyyvKd33xJJEhDXnv65+Z9vHS8uDu7GiHk4M986ffiY+HK9+t28WKX/fx6KgehPjXniFZtZkEZCGE\nqEZODa+821x1cPT1pcPLLxP72GPsee89jnz7bXno9YyOLr/18YUQbO/lVa+uryzE32FlZTjaYWtj\nfc0edH9vN54Z37/C8naRjdj5+ZOXTLswLAMgoXUY2z6dWt5znl9QRFFxCS2aGIZFdY4J5SvjzXBM\nhQYawmz3dhHl5RrD+PZz54sI8DYcIWge0pBbe7a9ZPjLufNF2Bp7l/cdPsn8lVuZMDi+Cq0iQAKy\nEELUK/YeHrR56ilzV0MIi2dlchUVV2cHWjat+ETSYD8Pgv08KiwPDfQ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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(columns=['z', 'waste', 'shortage', 'cost'])\n", "\n", "for i in np.arange(0.1, 20, 0.1):\n", " (D_real, D_pred, D_ordered, Waste, Shortage, \n", " fill_rate, error) = simulation_prediction(test, T_warm=14, m=5, z=i, sim_type='moving average')\n", "\n", " waste, short, cost = calculate_statistics(D_real, D_pred, D_ordered, Waste, \n", " Shortage, fill_rate, error, cw=100, cs=500, to_print=False)\n", " df.loc[len(df)] = [i, waste, short, cost]\n", "\n", "plot_all(df)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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zZIaxyihkrKwj42S97DZWdlauN4PErhWNgc1AIeBBSg+m0xuS9/ZpAxxO6T5E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\n9J85k6hBg/j0iis489Zb6T9zptfrdSw+Pj6O8ctmIOqQ8kF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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model = XGBRegressor(learning_rate=0.05062645559769, min_child_weight=10, max_depth=3,\n", " nr_estimators=100)\n", "model = train_model(train, model)\n", "\n", "df = pd.DataFrame(columns=['z', 'waste', 'shortage', 'cost'])\n", "\n", "for i in np.arange(0.1, 4, 0.1):\n", " (D_real, D_pred, D_ordered, Waste, Shortage, \n", " fill_rate, error) = simulation_prediction(test, T_warm=0, m=5, z=i, sim_type='prediction')\n", "\n", " waste, short, cost = calculate_statistics(D_real, D_pred, D_ordered, Waste, \n", " Shortage, fill_rate, error, cw=100, cs=500, to_print=False)\n", " df.loc[len(df)] = [i, waste, short, cost]\n", "\n", "plot_all(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both approaches show a similar type of graph. The costs decrease as it finds a more optimal z value, but increases as too much is being ordered. Similarly, as more is being ordered the percentage of waste increases and the shortage decreases. " ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.4" }, "latex_envs": { "LaTeX_envs_menu_present": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 2 }