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"cells": [
{
"cell_type": "markdown",
"id": "ace8f0fc-6e7b-4fbd-b684-961d18029819",
"metadata": {},
"source": [
"# Table of contents\n",
"1. [What would you find in this project?](#Whatwouldyoufindinthisproject?)\n",
"2. [Exploratory Data Analysis](#ExploratoryDataAnalysis)\n",
" 1. [Import data](#Importdata)\n",
" 2. [A First round of feature engineering](#FFE)\n",
" 3. [EDA for categorical features](#EDAC)\n",
" 4. [EDA for numerical features](#EDAN)\n",
" 5. [Check if holidays and events have an effect on sales](#holidays)\n",
" 6. [Check if the weekday has an effect on sales](#date)\n",
" 7. [Summary of EDA](#SUMMARY)\n",
"3. [Machine Learning](#ML)\n",
" 1. [Performance Metrics](#METRIC)\n",
" 2. [Split in training and test dataset. Compare both](#SPLIT)\n",
" 3. [Models](#Models)\n",
" 1. [Sarimax](#SARIMAX)\n",
" 2. [Prophet by Facebook](#PROPHET)\n",
" 3. [Xgboost](#XGBoost)\n",
" 4. [Model comparison](#COMPARISON)\n",
"4. [Production](#PRODUCTION)\n"
]
},
{
"cell_type": "markdown",
"id": "e56650e4-7206-46d3-91ba-8f84d27ddbb1",
"metadata": {},
"source": [
"\n",
"# What would you find in this project?\n",
"\n",
"The purpose of this project is to deal with a **real world forecasting problem** and approach it with a **business concern**: from explaining the insights we can get just from the data, find a ML model that would fit on the forecasting problem and finally put into production the model and the insights we can get from it.\n",
"\n",
"For this project I've chosen [this](https://www.kaggle.com/competitions/store-sales-time-series-forecasting/overview) Kaggle dataset. This dataset contains information about the sales of multiple stores of the company Favorita, in Ecuador. The goal of this project is to find a model that able to **predict the estimation of sales** for each family group of products and stores. Predicting the sales of an element (in this case a product) gives to any business and important and powerful information to take advantage on. In the case of the stores, one of these huge advantages could be able to **estimate future revenue**, **allocate resources more effectively**, etc. [Here](https://www.getweflow.com/blog/importance-of-sales-forecasting) for more information.\n",
"\n",
"So, in the following sections we will put ourselves in the shoes of a real store company, which want to understand which useful insights we can get from their historical data, train a model able to predict the future amount of sales and take advantage of this to increase the turnover."
]
},
{
"cell_type": "markdown",
"id": "1969ee8a-1c99-41d1-a8fe-8f98205149fb",
"metadata": {},
"source": [
"\n",
"# Exploratory Data Analysis\n",
"\n",
"In any ML project, the [first step](https://www.bitstrapped.com/blog/exploratory-data-analysis-accelerates-machine-learning) should be done an exploratory data analysis, not only because doing this will help us to understand better the data we're working with, but also let us know if there are problems in the data we're working on prior trying to model it, let us know that the model we've trained is reliable and finally is a first step to bring useful insights without even have to train a model."
]
},
{
"cell_type": "markdown",
"id": "58569916-a385-4d98-bac1-98fae261c66a",
"metadata": {},
"source": [
"\n",
"## Import data\n",
"\n",
"First of all, we'll import (and install) all necessary libraries we'll need in this notebook."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "5cef0062-8762-4da8-a9fb-e7edd5c9911d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\rojol\\anaconda3\\envs\\time_series\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Importing plotly failed. Interactive plots will not work.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from statsmodels.graphics.tsaplots import plot_pacf, plot_acf\n",
"from statsmodels.tsa.statespace.sarimax import SARIMAX \n",
"from statsmodels.tsa.seasonal import seasonal_decompose\n",
"import scipy.stats as stats\n",
"from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, RandomizedSearchCV\n",
"from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_log_error, make_scorer\n",
"import pmdarima as pm\n",
"from pmdarima.arima import ADFTest\n",
"from prophet import Prophet\n",
"from xgboost import XGBRegressor\n",
"import xgboost as xgb\n",
"from math import sqrt\n",
"from datetime import datetime, timedelta, date\n",
"import pickle\n",
"import logging\n",
"logging.getLogger(\"cmdstanpy\").disabled = True # turn 'cmdstanpy' logs off\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"id": "0441d729-4fac-46e7-afe5-efbbfd3da4ae",
"metadata": {},
"source": [
"Now, we can import the data and take a look to the first rows:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b4bfc9bc-d1c5-4d73-b370-dc113c4d7c26",
"metadata": {},
"outputs": [
{
"data": {
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" id date store_nbr family sales onpromotion\n",
"0 0 2013-01-01 1 AUTOMOTIVE 0.0 0\n",
"1 1 2013-01-01 1 BABY CARE 0.0 0\n",
"2 2 2013-01-01 1 BEAUTY 0.0 0\n",
"3 3 2013-01-01 1 BEVERAGES 0.0 0\n",
"4 4 2013-01-01 1 BOOKS 0.0 0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv('train.csv')\n",
"holidays_events_df = pd.read_csv('holidays_events.csv')\n",
"oil_df = pd.read_csv('oil.csv')\n",
"stores_df = pd.read_csv('stores.csv')\n",
"test = pd.read_csv('test.csv')\n",
"\n",
"train.head()"
]
},
{
"cell_type": "markdown",
"id": "62638f34-2dff-4936-9352-8e559d4de000",
"metadata": {},
"source": [
"Let's take a look to the columns of the train dataset:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d4c58e36-96c8-4099-8f72-ae2e372b7d48",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 3000888 entries, 0 to 3000887\n",
"Data columns (total 6 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 id int64 \n",
" 1 date object \n",
" 2 store_nbr int64 \n",
" 3 family object \n",
" 4 sales float64\n",
" 5 onpromotion int64 \n",
"dtypes: float64(1), int64(3), object(2)\n",
"memory usage: 137.4+ MB\n"
]
}
],
"source": [
"train.info()"
]
},
{
"cell_type": "markdown",
"id": "55fd7818-db0b-4bef-897e-adc5c8a8cd27",
"metadata": {},
"source": [
"In this project we have **several sources of information** that can be decisive to predict the sales of the Favorita supermarkets. In particular, we have the price daily oil price, which the kaggle project explains: \"Ecuador is an oil-dependent country and it's economical health is highly vulnerable to shocks in oil prices\", so could be interesting to add this information to the training set. On the other hand, we also have the national, regional and local events and public holidays, which are an important information to take into account in any forecasting model.\n",
"\n",
"So, for the above reasons, we'll join all this information into a single dataframe in order to manage all the features in an easier way.\n",
"\n",
"Anyway, we'll also study the effect of these features in the sales feature in order to know if it's a good idea to pick all these features to train the forecasting models."
]
},
{
"cell_type": "markdown",
"id": "54783cbc-0de2-4b3b-96e2-390b8dd99900",
"metadata": {},
"source": [
"\n",
"## A First round of feature engineering "
]
},
{
"cell_type": "markdown",
"id": "4db078c1-be1a-4239-aca7-a27c5a60192c",
"metadata": {},
"source": [
"In the following function, you can see how the dataframes with information about daily oil price, information about stores of Favorita company and the holidays and event dataframes are joined into the dataset of the daily sales for Favorita. Also, additional columns related with the date are added, as day of the week, day of the month, number of week and the number of month:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f53fa9ac-f448-42bc-8424-76cd16a48f77",
"metadata": {},
"outputs": [],
"source": [
"def join_multiple_df_sales(main_df, oil_df, stores_df, holidays_events_df):\n",
" # Make copies to not modify original inputs\n",
" oil_tmp = oil_df.copy(deep=True)\n",
" stores_tmp = stores_df.copy(deep=True)\n",
" holidays_events_tmp = holidays_events_df.copy(deep=True)\n",
" train_df_merge = main_df.copy(deep=True)\n",
" \n",
" # Convert from object to datetime 'date' column\n",
" train_df_merge['date']= pd.to_datetime(train_df_merge['date'])\n",
"\n",
" ## Merge with oil dataframe\n",
" oil_tmp['date']= pd.to_datetime(oil_tmp['date'])\n",
" # Fill null values for oil \n",
" oil_tmp.index = oil_tmp['date']\n",
" oil_tmp.drop(columns=['date'], inplace=True)\n",
" oil_tmp = oil_tmp.asfreq('D')\n",
" oil_tmp.interpolate(inplace=True)\n",
" # First value is null\n",
" oil_tmp.iloc[0]=oil_tmp.iloc[1]\n",
" train_df_merge = train_df_merge.merge(oil_tmp, left_on = 'date', right_index=True, how='left')\n",
" \n",
" ## Merge with store dataframe\n",
" train_df_merge = train_df_merge.merge(stores_tmp, left_on = 'store_nbr', right_on='store_nbr', how='left')\n",
" \n",
" ## Merge and process with holidays_events dataframe\n",
" holidays_events_tmp['date']= pd.to_datetime(holidays_events_tmp['date'])\n",
" # Local events and holidays\n",
" local_holidays = holidays_events_tmp[(holidays_events_tmp.locale == 'Local')][['date','locale_name']].rename(columns={\"locale_name\": \"is_local_hol_or_eve\"}).drop_duplicates()\n",
" train_df_merge = train_df_merge.merge(local_holidays, left_on = ['date','city'], right_on=['date','is_local_hol_or_eve'], how='left')\n",
" # Regional events and holidays\n",
" regional_holidays = holidays_events_tmp[(holidays_events_tmp.locale == 'Regional')][['date','locale_name']].rename(columns={\"locale_name\": \"is_regional_hol_or_eve\"}).drop_duplicates()\n",
" train_df_merge = train_df_merge.merge(regional_holidays, left_on = ['date','state'], right_on=['date','is_regional_hol_or_eve'], how='left') # National events and holidays\n",
" # National events and holidays\n",
" national_holidays = holidays_events_tmp[(holidays_events_tmp.locale == 'National')][['date','locale_name']].rename(columns={\"locale_name\": \"is_national_hol_or_eve\"}).drop_duplicates()\n",
" train_df_merge = train_df_merge.merge(national_holidays, left_on = ['date'], right_on=['date'], how='left')\n",
" # Cast to boolean columns\n",
" train_df_merge.is_local_hol_or_eve = train_df_merge.is_local_hol_or_eve.notnull().astype(int)\n",
" train_df_merge.is_regional_hol_or_eve = train_df_merge.is_regional_hol_or_eve.notnull().astype(int)\n",
" train_df_merge.is_national_hol_or_eve = train_df_merge.is_national_hol_or_eve.notnull().astype(int)\n",
"\n",
" # Add additional date features.\n",
" train_df_merge['day_of_week'] = train_df_merge['date'].dt.dayofweek\n",
" train_df_merge['day_of_month'] = train_df_merge['date'].dt.day\n",
" train_df_merge['month'] = train_df_merge['date'].dt.month\n",
" train_df_merge['week'] = train_df_merge['date'].dt.week\n",
" \n",
" return train_df_merge"
]
},
{
"cell_type": "markdown",
"id": "bc0c761e-b931-4b09-a565-f190d8bb6ede",
"metadata": {},
"source": [
"Below you can see the first rows of the train dataset merged with the other useful datasets:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2a225a2b-797a-4377-9c85-0994ca4df90b",
"metadata": {
"scrolled": true
},
"outputs": [
{
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0.0
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0
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93.14
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Pichincha
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"text/plain": [
" id date store_nbr family sales onpromotion dcoilwtico \\\n",
"0 0 2013-01-01 1 AUTOMOTIVE 0.0 0 93.14 \n",
"1 1 2013-01-01 1 BABY CARE 0.0 0 93.14 \n",
"2 2 2013-01-01 1 BEAUTY 0.0 0 93.14 \n",
"3 3 2013-01-01 1 BEVERAGES 0.0 0 93.14 \n",
"4 4 2013-01-01 1 BOOKS 0.0 0 93.14 \n",
"\n",
" city state type cluster is_local_hol_or_eve \\\n",
"0 Quito Pichincha D 13 0 \n",
"1 Quito Pichincha D 13 0 \n",
"2 Quito Pichincha D 13 0 \n",
"3 Quito Pichincha D 13 0 \n",
"4 Quito Pichincha D 13 0 \n",
"\n",
" is_regional_hol_or_eve is_national_hol_or_eve day_of_week day_of_month \\\n",
"0 0 1 1 1 \n",
"1 0 1 1 1 \n",
"2 0 1 1 1 \n",
"3 0 1 1 1 \n",
"4 0 1 1 1 \n",
"\n",
" month week \n",
"0 1 1 \n",
"1 1 1 \n",
"2 1 1 \n",
"3 1 1 \n",
"4 1 1 "
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},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = join_multiple_df_sales(train, oil_df, stores_df, holidays_events_df)\n",
"test_df = join_multiple_df_sales(test, oil_df, stores_df, holidays_events_df)\n",
"train_df.head()"
]
},
{
"cell_type": "markdown",
"id": "48759e7b-3af9-4bdc-a2c3-0e62fa2687a1",
"metadata": {},
"source": [
"\n",
"## Check for nulls or duplicated data\n",
"\n",
"\n",
"Another important thing is to check how many null data we have on the dataset. In case we only have a few nulls or to much nulls, we just can delete those records/feature, but otherwise we'll have to infer the missing data. Here you can see all the features with the total of null values:\n"
]
},
{
"cell_type": "code",
"execution_count": 145,
"id": "63248c93-74ae-4c7b-ae57-a398d6134bf1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"id 0\n",
"date 0\n",
"store_nbr 0\n",
"family 0\n",
"sales 0\n",
"onpromotion 0\n",
"dcoilwtico 0\n",
"city 0\n",
"state 0\n",
"type 0\n",
"cluster 0\n",
"is_local_hol_or_eve 0\n",
"is_regional_hol_or_eve 0\n",
"is_national_hol_or_eve 0\n",
"day_of_week 0\n",
"day_of_month 0\n",
"month 0\n",
"week 0\n",
"dtype: int64"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.isna().sum()"
]
},
{
"cell_type": "markdown",
"id": "01808794-2efa-4007-9c5a-9dbc9249b88b",
"metadata": {},
"source": [
"We've been lucky and there is no null data in our data. Let's check if there are any duplicated rows in our data, otherwise if we keep duplicated data in our data we could add bias in training data or inflate the apparent performance of your model during evaluation:"
]
},
{
"cell_type": "code",
"execution_count": 144,
"id": "ba82c57a-c5d5-4ac7-a050-7eedf026c6fc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.duplicated().any()"
]
},
{
"cell_type": "markdown",
"id": "5e986dec-df5f-4696-a217-7528eeaf5ca7",
"metadata": {},
"source": [
"As we can see above, there are no duplicated rows."
]
},
{
"cell_type": "markdown",
"id": "85fb5561-4685-4cb6-8853-49fa8328943a",
"metadata": {},
"source": [
"\n",
"## Exploratory Data Analysis of Sales for categorical features\n",
"\n",
"In this section we'll study the categorical features and the relation with the target feature Sales a to get some useful insights. We'll use the same custom function to plot all categorical features:"
]
},
{
"cell_type": "code",
"execution_count": 112,
"id": "2845c79d-2c67-4cb5-b879-4eae3d4a75b0",
"metadata": {},
"outputs": [],
"source": [
"# Function to get top N values of sales for a feature\n",
"def agg_top_n_values(df, feature, metric = 'sales', top_values=10, ascending = False):\n",
" # Group by dataframe\n",
" groupby_feature = df.groupby(feature)[[metric]].sum()\n",
" \n",
" # Sort values decreasing\n",
" category_counts = groupby_feature.sort_values(by = metric, ascending = ascending)\n",
"\n",
" #Check if there are more than top_values categories. If so, aggregate the rest in 'OTHER' category\n",
" if len(category_counts.index) > top_values:\n",
" # Take the top n categories\n",
" top_categories = category_counts.head(top_values)\n",
"\n",
" # Group other categories into a new 'Other' category and put in a new dataframe\n",
" other_category_count = pd.DataFrame([[category_counts[top_values:].sum().item()]], index=['OTHER'],columns=[metric])\n",
" \n",
" # Concatenate top categories and 'Other'\n",
" final_category_counts = pd.concat([top_categories, other_category_count], ignore_index=False)\n",
" \n",
" return final_category_counts\n",
" else:\n",
" return category_counts\n",
"\n",
"# Fucntion that plots a pie\n",
"def plot_pie_top_n_feature(df, feature, metric = 'sales', top_values=10):\n",
" # Aggregate dataframe in top 10 items and the rest in OTHER category\n",
" final_category_counts = agg_top_n_values(df, feature, metric, top_values)\n",
"\n",
" #Define color palette\n",
" colors = sns.color_palette('colorblind')\n",
" \n",
" # Plotting the pie chart\n",
" plt.figure(figsize=(8, 8))\n",
" plt.pie(final_category_counts[metric], labels=final_category_counts.index, autopct='%1.1f%%', pctdistance=0.8, startangle=0,labeldistance=1.155, wedgeprops = { 'linewidth' : 2, 'edgecolor' : 'white' }, colors=colors)\n",
" plt.title('Top {0} {1} values for {2} metric'.format(top_values, feature, metric), fontsize = 13)\n",
" plt.show()\n",
"\n",
"# Function that plots a time series by differents values of a feature\n",
"def plot_monthly_time_series_hue_top_n_feature(dataframe, feature, time_feature = 'date', metric = 'sales', top_values = 10):\n",
" # Filter columns\n",
" df_tmp = dataframe[[time_feature, feature, metric]]\n",
" df_tmp.set_index(time_feature, inplace=True)\n",
"\n",
" # Aggregate data for feature and in monthly data\n",
" df_tmp = df_tmp.groupby([pd.Grouper(freq='M'), feature]).sum().reset_index()\n",
"\n",
" # Get top n family products\n",
" df_top_n_values_features = agg_top_n_values(dataframe, feature, metric, top_values)\n",
" list_top_n_values_features = df_top_n_values_features.index.get_level_values(0).unique()\n",
"\n",
" # Filter dataframe with top 10 family products\n",
" df_top_n_values_features_w_time_metric = df_tmp[df_tmp[feature].isin(list_top_n_values_features)]\n",
"\n",
" # Line plot\n",
" plt.figure(figsize=(15,8))\n",
" color_palette = sns.husl_palette(top_values)\n",
" ax = sns.lineplot(x=time_feature, y=metric, hue=feature, data=df_top_n_values_features_w_time_metric, palette=color_palette)\n",
" ax.set_xlim(df_top_n_values_features_w_time_metric['date'].min(), df_top_n_values_features_w_time_metric['date'].max())\n",
" plt.title('Top {0} of {1} feature by monthly {2}'.format(top_values, feature, metric), fontsize = 15)"
]
},
{
"cell_type": "markdown",
"id": "30fbaea2-5965-43b2-96ed-a354fb2b3c93",
"metadata": {},
"source": [
"\n",
"### Analysis Family feature\n",
"The family feature identifies the type of product sold. Let's check which are the families of products top sold and how the sales of these products have evolved through the years."
]
},
{
"cell_type": "code",
"execution_count": 113,
"id": "f8520b83-5059-4bc8-8871-393c92e3803b",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_monthly_time_series_hue_top_n_feature(train_df, 'family')"
]
},
{
"cell_type": "markdown",
"id": "484adf05-6956-4396-9e29-9f4c80381263",
"metadata": {},
"source": [
"From the two plots we can see above, we can say the following:\n",
"- The top two family products more sold are: Grocery I and beverages. These two categories sum more than a 50% of the sales for the last years.\n",
"- Also, if we add the third and the forth categories sold: produce and cleaning, respectively, we almost have the 75% of the sales for the last years. So, **the top 4 category products make the 75% of the sales**, since 2013.\n",
"- Another interesting thing is that from the last 3 years until nowadays, produce has become the third category most sold. The order categories remain the same position of sales through all the years (since 2013)."
]
},
{
"cell_type": "markdown",
"id": "6aca5313-310e-4e98-a2f3-ac47a5bb4ee3",
"metadata": {},
"source": [
"\n",
"### Analysis Store Feature\n",
"The store_nbr feature identifies the store at which the products are sold. Let's see if there are specific store more important than others in terms of sales."
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "4c05f4a8-cb8b-45cf-ad9e-6fceaf4dfc9d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_monthly_time_series_hue_top_n_feature(train_df, 'store_nbr','date','sales',5)"
]
},
{
"cell_type": "markdown",
"id": "430dc535-fc3e-43cd-88f0-9989184cff81",
"metadata": {},
"source": [
"From the first plot we can see that the top store in terms of sales is the 44 and has almost the 6%, and even its the first one there is not an exaggerated difference with the rest of the stores. With this we mean that **no one of the stores is the principal source of income of the company** and if one them would fail (even the 44) wouldn't result in the closure of the company because only would affect the 6% of the sales. Even though is clear that depends on the store our model would predict different amount of sales.\n",
"\n",
"On the other hand, from the second plot we can say that there has been a **growth in sales in the last few years**."
]
},
{
"cell_type": "markdown",
"id": "d36929a5-347e-422e-a006-115ed7a5cd74",
"metadata": {},
"source": [
"\n",
"### Analysis State Feature\n",
"The state feature is a metadata that identifies the ecuatiorian state where the stores are located."
]
},
{
"cell_type": "code",
"execution_count": 154,
"id": "d8bd9c46-7700-4c62-9ff3-307330bc0450",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_monthly_time_series_hue_top_n_feature(train_df, 'state','date','sales',5)"
]
},
{
"cell_type": "markdown",
"id": "9ce081b1-4bef-43c1-8462-79d1e1a0bf0c",
"metadata": {},
"source": [
"From the two plots we can see above, we can say the following:\n",
"- The top state with more sales is: Pachincha. In this only state are made the 54.5% of sales. So we can say that for Favorita company this state has a lot of weight in their income. \n",
"- Also, if we add the second and the third state: Guayas and Azuay, all of them sum more than the 75% of the sales. So, this top three states make the 75% of sales."
]
},
{
"cell_type": "markdown",
"id": "8289ba1b-b57c-4780-bf26-2ebdd7cc9fbb",
"metadata": {},
"source": [
"\n",
"### Analysis Holidays Features\n",
"In this section we want to discover if holidays and events have an effect on sales. To do so, we'll compare the distribution and the principal statistics of the sales for those days that are holidays and for those that aren't. To do so, we've built a function that plot a boxplot and show principal statistical metrics:"
]
},
{
"cell_type": "code",
"execution_count": 135,
"id": "694ba4b8-ed28-4623-bce2-a16062a33226",
"metadata": {},
"outputs": [],
"source": [
"def compare_main_stadistical_metrics(**lists):\n",
" df_template = pd.DataFrame(columns=['count', 'std', 'min','mean','percentil 50', 'percentil 90', 'percentil 95','percentil 99', 'max'])\n",
" df = df_template.copy()\n",
"\n",
" elements = len(lists)\n",
" nrows = elements//2\n",
" if elements%2 != 0:\n",
" nrows +=1\n",
" fig, axes = plt.subplots(nrows = nrows, ncols = 2, figsize=(10,10))\n",
" i = 0\n",
" for list_name, list in lists.items():\n",
" df.loc[list_name] = [list.count().item(), list.std().item(), list.min().item(), list.mean().item(), list.quantile(0.5).item(), list.quantile(0.9).item(), list.quantile(0.95).item(), list.quantile(0.99).item(), list.max().item()]\n",
" ax = sns.boxplot(data=list, ax=axes.flatten()[i])\n",
" i+=1\n",
" plt.show()\n",
" return df\n"
]
},
{
"cell_type": "markdown",
"id": "35bd3ca3-a470-4b56-90de-946442dd2d23",
"metadata": {},
"source": [
"Now, we want to make subsets of data for those days that are holidays and for those that aren't holidays as you can see below:"
]
},
{
"cell_type": "code",
"execution_count": 136,
"id": "9786c125-c23a-4051-90e1-b8971556c368",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
],
"text/plain": [
" count std min \\\n",
"sales_without_any_holiday 1541.0 226668.778251 185222.899016 \n",
"sales_with_national_holiday 143.0 286172.032538 2511.618999 \n",
"\n",
" mean percentil 50 percentil 90 \\\n",
"sales_without_any_holiday 625043.604085 622771.923903 9.356560e+05 \n",
"sales_with_national_holiday 735212.394940 737819.484836 1.087337e+06 \n",
"\n",
" percentil 95 percentil 99 max \n",
"sales_without_any_holiday 1.039418e+06 1.185140e+06 1.438311e+06 \n",
"sales_with_national_holiday 1.194662e+06 1.329448e+06 1.402306e+06 "
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_without_any_holiday = train_df[(train_df.is_local_hol_or_eve == 0) & (train_df.is_regional_hol_or_eve == 0) & (train_df.is_national_hol_or_eve == 0)][['sales','date']].groupby('date')[['sales']].sum().rename(columns={\"sales\": \"sales without holidays\"})\n",
"sales_with_national_holiday = train_df[(train_df.is_national_hol_or_eve == 1)][['sales','date']].groupby('date')[['sales']].sum().rename(columns={\"sales\": \"sales with national holidays\"})\n",
"compare_main_stadistical_metrics(sales_without_any_holiday=sales_without_any_holiday, sales_with_national_holiday=sales_with_national_holiday) "
]
},
{
"cell_type": "markdown",
"id": "8a3108f4-90b7-4f79-988f-620ae8ecdc0d",
"metadata": {},
"source": [
"The first thing we can note is that the boxplot are noticeably different. The median from the boxplot sales without holidays is lower than the percentile 25 of the sales with national holidays and the median of the sales for national days is greater than percentile 75 for regular days, so just with this information we can guess that **holidays have a positive effect on sales**. To confirm that effect, we'll perform a t-test on the sales for holidays and for regular days. The T-test will confirm us if there is a significant difference between the mean of the two sets of sales:"
]
},
{
"cell_type": "code",
"execution_count": 134,
"id": "29902631-ca69-4fcb-b833-1a96c00a2adb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-Statistic: [-4.47519228]\n",
"P-Value: [1.44864315e-05]\n",
"Reject the null hypothesis: There is a significant difference in sales.\n"
]
}
],
"source": [
"# Generate sample data for regular days and holiday sales\n",
"np.random.seed(42) # for reproducibility\n",
"regular_days_sales = sales_without_any_holiday\n",
"holiday_sales = sales_with_national_holiday\n",
"\n",
"# Perform independent samples t-test\n",
"t_statistic, p_value = stats.ttest_ind(regular_days_sales, holiday_sales, equal_var = False)\n",
"\n",
"# Output the results\n",
"print(f'T-Statistic: {t_statistic}')\n",
"print(f'P-Value: {p_value}')\n",
"\n",
"# Compare p-value to significance level (e.g., 0.05)\n",
"alpha = 0.05\n",
"if p_value < alpha:\n",
" print('Reject the null hypothesis: There is a significant difference in sales.')\n",
"else:\n",
" print('Fail to reject the null hypothesis: There is no significant difference in sales.')"
]
},
{
"cell_type": "markdown",
"id": "fb1688a4-280a-4071-b2f5-f4077d40e268",
"metadata": {},
"source": [
"So, as we guessed earlier, there is a significant difference in sales for those days that are holidays and those days that are regular, for that reason, we should take into account the holidays days in our model."
]
},
{
"cell_type": "markdown",
"id": "a6b17297-75b9-425b-a87c-e23231f2970b",
"metadata": {},
"source": [
"\n",
"### Analysis Date Features\n",
"In this section we want to discover if the week date has an effect on sales. To do so, we'll compare the distribution and the principal statistics of the sales all the week days. To do so, we'll use the same function used in the previous section:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "88980dbc-4dc4-44c5-9b6a-f6b9f45839b0",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
],
"text/plain": [
" count std min mean \\\n",
"monday_sales 241.0 202901.445868 278627.588926 617542.713052 \n",
"tuesday_sales 242.0 199672.002474 2511.618999 569926.087922 \n",
"wednesday_sales 240.0 206976.013112 8602.065404 593244.553472 \n",
"thursday_sales 240.0 168677.994700 12773.616980 505269.201115 \n",
"friday_sales 240.0 191907.914811 16433.394000 579574.361085 \n",
"saturday_sales 241.0 221747.538455 403258.212011 772205.593943 \n",
"sunday_sales 240.0 257472.920530 12082.500997 825218.121579 \n",
"\n",
" percentil 50 percentil 90 percentil 95 percentil 99 \\\n",
"monday_sales 651800.509461 8.223347e+05 8.968249e+05 1.213175e+06 \n",
"tuesday_sales 602440.102498 7.917177e+05 9.061189e+05 1.094547e+06 \n",
"wednesday_sales 632951.683756 8.415648e+05 8.878679e+05 1.049797e+06 \n",
"thursday_sales 531402.203129 6.949932e+05 7.673973e+05 9.868689e+05 \n",
"friday_sales 616645.013015 8.172151e+05 8.571297e+05 9.589227e+05 \n",
"saturday_sales 816064.644983 1.052868e+06 1.103806e+06 1.196268e+06 \n",
"sunday_sales 862268.400242 1.140642e+06 1.202526e+06 1.274494e+06 \n",
"\n",
" max \n",
"monday_sales 1.402306e+06 \n",
"tuesday_sales 1.152089e+06 \n",
"wednesday_sales 1.196146e+06 \n",
"thursday_sales 1.135849e+06 \n",
"friday_sales 1.282146e+06 \n",
"saturday_sales 1.463084e+06 \n",
"sunday_sales 1.376512e+06 "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monday_sales = train_df[train_df['date'].dt.dayofweek == 0].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"monday\"})\n",
"tuesday_sales = train_df[train_df['date'].dt.dayofweek == 1].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"tuesday\"})\n",
"wednesday_sales = train_df[train_df['date'].dt.dayofweek == 2].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"wednesday\"})\n",
"thursday_sales = train_df[train_df['date'].dt.dayofweek == 3].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"thursday\"})\n",
"friday_sales = train_df[train_df['date'].dt.dayofweek == 4].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"friday\"})\n",
"saturday_sales = train_df[train_df['date'].dt.dayofweek == 5].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"saturday\"})\n",
"sunday_sales = train_df[train_df['date'].dt.dayofweek == 6].groupby(by=[\"date\"]).sum()['sales'].to_frame().rename(columns={\"sales\": \"sunday\"})\n",
"compare_main_stadistical_metrics(monday_sales=monday_sales, tuesday_sales=tuesday_sales, wednesday_sales=wednesday_sales, thursday_sales=thursday_sales, friday_sales=friday_sales, saturday_sales=saturday_sales, sunday_sales=sunday_sales)"
]
},
{
"cell_type": "markdown",
"id": "9a0b287d-579c-4c63-b802-873295848029",
"metadata": {},
"source": [
"Again, we can notice a difference between the sale made during all the days of the week, especially for Thursdays (that is the day with less sales, 50K on average) and Sunday (that is the day with more sales, 85K on average). To confirm that the day of the week has an impact on the sales, let's perform again a T-test with the sales on Thursday and Sunday:"
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "917c7ccb-8d58-44bf-8941-efc9b6c7c4f8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T-Statistic: [-16.10308569]\n",
"P-Value: [1.30758191e-45]\n",
"Reject the null hypothesis: There is a significant difference in sales.\n"
]
}
],
"source": [
"# Generate sample data for regular days and holiday sales\n",
"np.random.seed(42) # for reproducibility\n",
"\n",
"# Perform independent samples t-test\n",
"t_statistic, p_value = stats.ttest_ind(thursday_sales, sunday_sales, equal_var = False)\n",
"\n",
"# Output the results\n",
"print(f'T-Statistic: {t_statistic}')\n",
"print(f'P-Value: {p_value}')\n",
"\n",
"# Compare p-value to significance level (e.g., 0.05)\n",
"alpha = 0.05\n",
"if p_value < alpha:\n",
" print('Reject the null hypothesis: There is a significant difference in sales.')\n",
"else:\n",
" print('Fail to reject the null hypothesis: There is no significant difference in sales.')"
]
},
{
"cell_type": "markdown",
"id": "ef25b53a-aa31-4468-825b-22be4f9cc6c1",
"metadata": {},
"source": [
"So, as we guessed earlier, there is a significant **difference in sales depend on the day of the week**, for that reason, we should take into account the week day in our model."
]
},
{
"cell_type": "markdown",
"id": "2028a5be-bc47-44ce-9586-cef7d750575a",
"metadata": {},
"source": [
"\n",
"## Exploratory Data Analysis of Sales for numerical features"
]
},
{
"cell_type": "markdown",
"id": "f93ae5b5-4129-4f36-912f-15ad498d8d15",
"metadata": {},
"source": [
"Let's explore the two numerical features we have in this dataset: \n",
"- **onpromotion**: the total number of items in a product family that were being promoted at a store at a given date.\n",
"- **dcoilwtico**: daily oil price\n",
"\n",
"Because the feature dcoilwtico is given daily, we'll sum and aggregate sales and onpromotion features on a daily basis in order to compare with the same measure all these three features."
]
},
{
"cell_type": "code",
"execution_count": 140,
"id": "aff28025-650c-45fe-8d92-701c01258600",
"metadata": {},
"outputs": [],
"source": [
"# Aggregate daily sales and onpromotion\n",
"groupby_date_sales_promotion = train_df.groupby('date')[['sales','onpromotion']].sum()\n",
"\n",
"# Set datetime index to oil datarame\n",
"oil_df['date']= pd.to_datetime(oil_df['date'])\n",
"oil_df = oil_df.set_index('date')\n",
"\n",
"# Join all features in same dataset\n",
"groupby_date_sales_promotion_oil = groupby_date_sales_promotion.merge(oil_df, left_index = True, right_on='date', how='left')"
]
},
{
"cell_type": "markdown",
"id": "fac12aa7-f54c-4fa8-916a-7078a5c9d4d8",
"metadata": {},
"source": [
"Now that we have all data aggregated daily, let's compute the correlation to see the relationship between all these features. Because we're dealing with continous variable we'll use pearson correlation:"
]
},
{
"cell_type": "code",
"execution_count": 143,
"id": "2a0ebbc4-fa53-4175-b359-4b92a456b8a9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corrmat = groupby_date_sales_promotion_oil[['sales','onpromotion','dcoilwtico']].corr()\n",
"f, ax = plt.subplots(figsize=(6,4))\n",
"sns.heatmap(corrmat, vmax=.8, square=True,annot=True)"
]
},
{
"cell_type": "markdown",
"id": "4358ff18-d776-4ea5-aaca-94b65a6429c0",
"metadata": {},
"source": [
"As we can see above, sales feature has a [high degree of correlation](https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/pearsons-correlation-coefficient/) with the price of oil and the number of items on promotion because both of them are greater than 0.5 (in absolute value). \n",
"\n",
"In the case of onpromotion feature, for greater values of this feature also we find high values for sales, which makes sense because if a store makes a deal or an offer of a product it is supposed to increase sales. \n",
"\n",
"On the other hand, for dcoiltwico feature, because presents a negative correlation, for greater values of oil, lower values of sales. This also makes sense, because for greater values of oil, the cost of living also increases (transportation cost increases the price of products) and that's why is reasonable that sales decreases.\n",
"\n",
"These two features have an impact on our target feature sales, so we'll include them in the models."
]
},
{
"cell_type": "markdown",
"id": "a18d595a-7d27-4746-b325-205c66bd0f3e",
"metadata": {},
"source": [
"\n",
"## Check trend and seasonality\n",
"\n",
"In the case of forecasting is very important to know if data is stationary and which is the trend and the seasonality. It is important to know because depend on them, there will be models that wouldn't fit at all.\n",
"\n",
"We're going to aggregate data daily because it would be easier to plot the time series and detect any pattern in data:"
]
},
{
"cell_type": "code",
"execution_count": 147,
"id": "d46bcd33-a1a9-4ea3-a914-54eb948d9a79",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
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"
sales
\n",
"
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"
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"
date
\n",
"
\n",
"
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" \n",
" \n",
"
\n",
"
2013-01-01
\n",
"
2511.618999
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"
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"
\n",
"
2013-01-02
\n",
"
496092.417944
\n",
"
\n",
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\n",
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2013-01-03
\n",
"
361461.231124
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"
\n",
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\n",
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2013-01-04
\n",
"
354459.677093
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2013-01-05
\n",
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477350.121229
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"
],
"text/plain": [
" sales\n",
"date \n",
"2013-01-01 2511.618999\n",
"2013-01-02 496092.417944\n",
"2013-01-03 361461.231124\n",
"2013-01-04 354459.677093\n",
"2013-01-05 477350.121229"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_sales = train_df.groupby('date')[['sales']].sum()\n",
"daily_sales.head()"
]
},
{
"cell_type": "markdown",
"id": "b4fdf46f-598b-429a-8f2a-0cc211141c1c",
"metadata": {},
"source": [
"The following function will help us to visualize the time series and the correlation with its lagged values (refers to the value of a variable at a previous time point), using the Autocorrelation plot and the partial autocorrelation plot. A strong correlation at a particular lag indicates that the series is correlated with itself at that lag, suggesting a potential pattern or dependency in the data.\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "9311f625-6fef-45c6-9e06-c67d10e3b2d6",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_time_series_acf_pacf(data, lags=None):\n",
" plt.figure(figsize=(8,8))\n",
" layout = (3,1)\n",
" raw = plt.subplot2grid(layout, (0, 0))\n",
" acf = plt.subplot2grid(layout, (1, 0))\n",
" pacf = plt.subplot2grid(layout, (2, 0))\n",
" \n",
" raw.plot(data)\n",
" plot_acf(data, lags=lags, ax=acf, zero=False)\n",
" plot_pacf(data, lags=lags, ax=pacf, zero = False)\n",
" sns.despine()\n",
" plt.tight_layout()\n",
"\n",
"plot_time_series_acf_pacf(daily_sales['sales'], lags = 28)"
]
},
{
"cell_type": "markdown",
"id": "3d2c4398-c507-404c-92a6-8e8167948190",
"metadata": {},
"source": [
"From the plot of the time series, we can say that a **linear positive trend** can be seen, with a multiplicative seasonal component (because seasonal fluctuations vary proportionally with the trend level). \n",
"On the other hand, **weekly seasonality** can be appreciated with the correlation plots. For both of them, you can see peaks for lags multiples of 7.\n",
"\n",
"Visually, it doesn't seem that time series is stationary, because the mean (and others statistical metrics) of the time series don't remain constant over time. Anyway, let's compute the ADF test to confirm it."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "475847c1-9fde-4ff8-842a-e75b45b6225f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.01, False)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"adf_test=ADFTest(alpha=0.05)\n",
"adf_test.should_diff(daily_sales)"
]
},
{
"cell_type": "markdown",
"id": "c4c7f503-d3a9-4549-9a41-a4c23b4d2667",
"metadata": {},
"source": [
"The test confirm the non-stationary of the time series."
]
},
{
"cell_type": "markdown",
"id": "e04fd05f-e65a-416a-8406-5ab931248072",
"metadata": {},
"source": [
"\n",
"## Summary EDA\n",
"\n",
"In this section we've said many things about this dataset. Let's just summarize all above in the following take aways:\n",
"- The top 4 category products (grocery I, beverages, produce and cleaning) make the 75% of the sales \n",
"- No one of the stores is the principal source of income of the company. The store with more sales only have the 6% of the generals sales.\n",
"- There are three states that makes the 75% of the sales. These states are: Pachincha, Guayas and Azuay\n",
"- Holidays have a positive effect on sales.\n",
"- The day of the week have and impact on the sales, especially for Thursdays (that is the day with less sales, 50K on average) and Sunday (that is the day with more sales, 85K on average)\n",
"- When there are products on promotion the sales increase.\n",
"- When the oil prices increases, the sales decreases.\n",
"- We've seen a linear positive trend in sales and a weekly seasonality.\n"
]
},
{
"cell_type": "markdown",
"id": "30b57f79-a9ac-4873-ba59-ce8b2d1d8452",
"metadata": {},
"source": [
"\n",
"# Machine Learning\n",
"\n",
"In the previous part of this project, we've had examined a dataset that describes the sales of different stores located in Ecuador. We explored and discovered interesting insights about the sales of the company and how the external factors can influence the sales of the company. Now, we're moving on to the Machine Learning part of this project.\n",
"\n",
"Our main goal here is to find the best model that could accurately **predict the sales of each store and product of the company**. Also, in this section we'll clean and transform the data to ease that the data could fit in several models.\n"
]
},
{
"cell_type": "markdown",
"id": "4362489d-b2e0-4cb4-8838-d32fc671cae7",
"metadata": {},
"source": [
"\n",
"## Performance Metric\n",
"\n",
"For this forecasting problem we'll be using these following three performance metrics:\n",
"\n",
" - **MAE** (Mean absolute error). This metric is the average of absolute difference between forecasted values and true values. This metric is not sensitive to outliers.\n",
" - **R2** (Coefficient of determination). This metric tries to measure how good or how bad is the model respect to a model that always returns the mean of the target. This metric is upper bounded by 1, and for R2 values near 1 it means that our model is far much better that the mean estimator. For values around 0, means that our model performs likewise the mean estimator. This metric is not lower bounded.\n",
" - **RMSLE** (Root Mean Squared Logarithmic Error). Penalize underestimates more than overestimates. This is the metric that uses the Kaggle competition, so let's use it as well.\n",
"\n",
"Another metric that would be interesting to use would be **MAPE** (Mean Absolute Percentage Error), which is the percentage of the average of absolute difference between forecasted values and true values, divided by true value. Because in our dataset there are a lots of zero values to forecast is not a good idea to use this metric in our case.\n",
" \n",
"We'll be using both metrics to check how the trained models are performing."
]
},
{
"cell_type": "markdown",
"id": "50562cbf-f730-4ff3-9c8b-4c152ca33d40",
"metadata": {},
"source": [
"\n",
"## Split training and test data\n",
"\n",
"For time series, data never should be shuffled and always we should take as validation/test data the last records of the series, in order to check if our model could properly forecast the next steps of the time series. \n",
"\n",
"Because the hardware resources for this project are limited, we'll be using either the last month or last year for historical data. We'll pick the last 15 days for testing purposes because in the kaggle competition this is the range to forecast."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "36abf2d7-d083-4e2b-87f9-3f8fea892558",
"metadata": {},
"outputs": [],
"source": [
"# Split into training and testing\n",
"tmp = train_df[(train_df.date >= '2016-08-01')] # Filter last year of historical\n",
"# Set date as index\n",
"tmp.index = tmp.date\n",
"tmp.date.drop(columns=['date'], inplace=True)\n",
"# Define two differents training sets\n",
"df_train_year = tmp[(tmp.index >= '2016-08-01') & (tmp.index < '2017-08-01')]\n",
"df_train_month = tmp[(tmp.index >= '2017-07-01') & (tmp.index < '2017-08-01')]\n",
"# Define the test set\n",
"df_test = tmp[(tmp.index >= '2017-08-01')]"
]
},
{
"cell_type": "markdown",
"id": "eb6dece6-32c8-4102-8811-2f4fe8102b34",
"metadata": {},
"source": [
"Also, let's create an empty dataframe where we'll store all the errors metrics of our models."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1b89c81c-70ae-4322-92ff-c6832dad1936",
"metadata": {},
"outputs": [],
"source": [
"# Create empty DataFrame to store the performance metrics of each model\n",
"df_results = pd.DataFrame()"
]
},
{
"cell_type": "markdown",
"id": "7581ec8f-47b5-4e6b-9c65-e505a3c469ba",
"metadata": {},
"source": [
"\n",
"## Models\n",
"\n",
"In this section, we'll try to find a model able to predict the house price sale based on all the features. To do so, we'll use a hyperparameter tuning and a cross validation. "
]
},
{
"cell_type": "markdown",
"id": "711bee20-1a77-4626-a43e-b4b49f0093b6",
"metadata": {},
"source": [
"\n",
"### SARIMAX \n",
"\n",
"Sarimax could be a good fit for our data because this model takes into account **seasonality components and also exogenous** (i.e. external) variables can be added to the model. \n",
"\n",
"Sarimax model is an extension of the classical Sarima model that also incorporates exogenous variables (external predictors that are independent of the model's own time series). So, to understand Sarimax, let's explain briefly the basics of Sarima.\n",
"\n",
"**Sarima** comes from **Arima** model which is a compound of different models: AR (autoregression), I (integration), MA (moving average). Let's explain a little bit each one:\n",
"\n",
"1. **AR**: models the linear relationship between the current observation and a number of $p$ lagged values. In simpler terms, captures the dependency between current values and its past values.\n",
"2. **I**: refers to the differencing process to make data stationary. The order of differencing is given by a parameter $d$ usually $\\in \\{0,1,2\\}$. It means the number of times of differencing needed to make data stationary. Data is converted from non-stationary to stationary by subtracting previous observations.\n",
"3. **MA**: Models the relationship between current observation and its past $q$ errors in the forecast. It assumes that there is information left in the residuals that can be added into the model\n",
"\n",
"To get an estimation for the p and q parameters, can be useful to take a look to the autocorrelation (ACF) and partial autocorrelation functions (PACF). To pick a p value get the peaks from PACF or the greatest value above the 0.95 confidence interval. To pick q, do the same but with the ACF.\n",
"\n",
"The whole process for Arima would be like this:\n",
"1. Remove trend of the time series by differencing (**I**)\n",
"2. Apply **AR**: Build the linear forecast method with lagged values.\n",
"3. Apply **MA**:\n",
" 1. Find residuals/errors $y-\\hat y$\n",
" 2. Build forecast model on residuals\n",
" 3. Update initial forecast with the forecasted residuals\n",
"4. De-differencing: adding back lagged values to our forecast (**I**)\n",
" \n",
"Also, the \"**S**\" from Sarima indicates the \"seasonal\" part. The model accounts for recurring patterns, like monthly or yearly seasonality. This component is modeled by 4 parameters:\n",
"1. **P** (Seasonal AutoRegressive): Similar to p, but for seasonal effects. How many past seasonal values influence the forecast.\n",
"2. **D** (Seasonal Integrated): How many times to seasonally difference the data to remove seasonal trends.\n",
"3. **Q** (Seasonal Moving Average): How many past seasonal forecast errors are considered.\n",
"4. **m** (Seasonality): This defines the number of periods in a season. For monthly data, m=12.\n",
"\n",
"Finally, for Sarimax model, the \"**X**\" comes from e**x**ogenous. SARIMAX allows for the inclusion of external variables (exogenous regressors) that can improve the model's forecasting performance. These variables are not directly influenced by the time series itself but may impact its behavior.\n",
"\n",
"For hardware and time performance limitations, data must be partitioned and trained a separately different sarimax models for every partition. These partitions will be done with every combination of store number and family product.\n",
"\n",
"In the next cell, you can see a train and a scoring function. In the training function a sarimax is trained for each combination of store and family product with the best hyperparameters in each partition. Because in the EDA section we've seen a weekly seasonality, we've set m = 7.\n",
"\n",
"For the scoring function, MAE, R2 and RMSLE are calculated for each model and then a general MAE,R2 and RMSLE are calculated.\n",
"\n",
"At first, we'll try to fit data with a monthly historical range."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "78983972-abf7-4b15-afba-c602f0d19671",
"metadata": {},
"outputs": [],
"source": [
"def train_multiple_sarimax_models(df_train, file_to_store_models, exog_features = ['onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week'], models_sarimax = {}):\n",
" # Dict where we'll store all models: models_sarimax\n",
" # Retrieve all stores and family groups products for which combinations we'll create a model\n",
" store_list = df_train.store_nbr.sort_values().unique()\n",
" family_group = df_train.family.sort_values().unique()\n",
" cont = 0\n",
" for store in store_list:\n",
" for family in family_group:\n",
" model = str(store) + \"_\" + family\n",
" if model not in models_sarimax.keys():\n",
" # Filter data to train model for specific store and family\n",
" df_train_tmp = df_train[(df_train.store_nbr == store) & (df_train.family == family)]\n",
" # Try ocsb value for seasonal_test by default. If fails use ch\n",
" for seasonal_test in ['ocsb','ch']:\n",
" try:\n",
" # Get better ARIMA hiperparameters for this model\n",
" stepwise_model = pm.auto_arima(df_train_tmp['sales'], \n",
" exog=df_train_tmp[exog_features],\n",
" start_p=0, max_p=3,\n",
" start_q=0, max_q=3, \n",
" m=7, \n",
" seasonal=True,\n",
" d=None,\n",
" start_P=0, max_P=3, \n",
" start_Q=0, max_Q=3,\n",
" max_D=2,\n",
" trace=False,\n",
" error_action='ignore', \n",
" seasonal_test= seasonal_test,\n",
" suppress_warnings=True, \n",
" n_jobs = -1,\n",
" stepwise=True)\n",
" order = stepwise_model.order\n",
" seasonal_order = stepwise_model.seasonal_order\n",
" # Train SARIMAX model with best hiperparameters found\n",
" sar = SARIMAX(df_train_tmp['sales'], \n",
" exog=df_train_tmp[exog_features],\n",
" seasonal_order=seasonal_order).fit()\n",
" # Save model trained in dictionary\n",
" models_sarimax[model] = sar\n",
" # Serialize and write the variable to the file\n",
" with open(file_to_store_models, 'wb') as file:\n",
" pickle.dump(models_sarimax, file) \n",
" break #if worked break seasonal_test loop \n",
" except:\n",
" print('For {} store and {} family model, used ch as seasonal_test argument'.format(store,family))\n",
" cont += 1\n",
" if cont%100 == 0:\n",
" print(cont) \n",
" return models_sarimax\n",
"#https://stackoverflow.com/questions/71944110/max-lag-problem-using-auto-arima-function --> Why I use diffent values for seasonal_test \n",
"\n",
"def sarimax_metric_errors(models_sarimax, df_test, exog_features = ['onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week']):\n",
" # Initializate lists to store results\n",
" forecast_values = pd.Series([])\n",
" real_values = pd.Series([])\n",
" r2_values = []\n",
" mae_values = []\n",
" models_name = []\n",
" rmsle_values = []\n",
" from sklearn.metrics import mean_squared_log_error\n",
" \n",
" store_list = df_test.store_nbr.sort_values().unique()\n",
" family_group = df_test.family.sort_values().unique()\n",
" for store in store_list:\n",
" for family in family_group:\n",
" model = str(store) + \"_\" + family\n",
" if model in models_sarimax.keys():\n",
" df_test_tmp = df_test[(df_test.store_nbr == store) & (df_test.family == family)]\n",
" forecast = models_sarimax[model].get_forecast(steps=15, exog=df_test_tmp[exog_features])\n",
" future_predicted_values = forecast.predicted_mean\n",
" actual_values = df_test_tmp['sales']\n",
"\n",
" # Append all forecast and real values for general metric errors\n",
" forecast_values = forecast_values.append(future_predicted_values)\n",
" real_values = real_values.append(actual_values)\n",
" \n",
" # Calculate error metrics for each specific model\n",
" models_name.append(model)\n",
" r2_scr = r2_score(actual_values,future_predicted_values)\n",
" # Because r2 is not lower bounded, for visualizig purposes set 0 all those negative r2 scores.\n",
" if r2_scr <= 0:\n",
" r2_scr = 0\n",
" r2_values.append(r2_scr)\n",
" mae_values.append(mean_absolute_error(actual_values,future_predicted_values))\n",
" future_predicted_values = [0 if i < 0 else i for i in future_predicted_values] #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle_values.append(sqrt(mean_squared_log_error(actual_values,future_predicted_values)))\n",
"\n",
" # Save in a dataframe the performance metric for each model\n",
" data = {\n",
" 'r2': r2_values,\n",
" 'mae': mae_values,\n",
" 'rmsle': rmsle_values\n",
" }\n",
" df = pd.DataFrame(data, index=models_name)\n",
" # create histogram with a different y scale for different rows\n",
" selection = ['r2', 'mae','rmsle']\n",
" fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14,4))\n",
" for i, col in enumerate(selection):\n",
" ax = sns.histplot(data=df, x=col, ax=axes.flatten()[i], bins = 50)\n",
" ax.set_ylabel('value')\n",
" ax.set_xlabel(col)\n",
" plt.show()\n",
"\n",
" # Compute general metrics performance \n",
" r2 = r2_score(real_values,forecast_values)\n",
" print(\"\\nGeneral value for R2 metric for all models is: \" + str(r2))\n",
" mae = mean_absolute_error(real_values,forecast_values)\n",
" print(\"\\nGeneral value for MAE metric for all models is:\" + str(mae))\n",
" forecast_values[forecast_values < 0] = 0 #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle = sqrt(mean_squared_log_error(real_values,forecast_values))\n",
" print(\"\\nGeneral value for RMSLE metric for all models is:\" + str(rmsle))\n",
"\n",
" return r2, mae, rmsle, df"
]
},
{
"cell_type": "markdown",
"id": "72cb9f62-02de-41e1-8c05-97555c7cf177",
"metadata": {},
"source": [
"\n",
"#### Monthly historical data\n",
"\n",
"Train the model or load trained model from disk."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c273a4a9-7a66-41cc-9ef2-264bb6f3ea9a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 15.4 s\n",
"Wall time: 16.7 s\n"
]
}
],
"source": [
"%%time \n",
"# 1h 10min\n",
"file_sarimax_month_path = 'sarima_month_trained.pickle'\n",
"# models_sarimax_month = train_multiple_sarimax_models(df_train_month, file_sarimax_month_path)\n",
"# If we've already trained the model and we want it to restore from file instead to retrain again.\n",
"with open(file_sarimax_month_path, 'rb') as file:\n",
" # Deserialize and retrieve the variable from the file\n",
" models_sarimax_month = pickle.load(file)"
]
},
{
"cell_type": "markdown",
"id": "e8722b2e-70d8-4acf-b744-be658e6f31d1",
"metadata": {},
"source": [
"Let's see the performance metrics of all models trainned and the general performance metrics:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "72df6c8d-2058-4bcd-84d9-22a80b16587b",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"General value for R2 metric for all models is: 0.9093748919697401\n",
"\n",
"General value for MAE metric for all models is:106.06005187095916\n",
"\n",
"General value for RMSLE metric for all models is:0.6215412629164062\n",
"CPU times: total: 24.6 s\n",
"Wall time: 24.9 s\n"
]
}
],
"source": [
"%%time\n",
"r2_sarimax_month, mae_sarimax_month, rmsle_sarimax_month, all_models_metric_sarimax_month = sarimax_metric_errors(models_sarimax_month, df_test)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d80e567c-d04c-4aa1-90e8-9d287f8a942c",
"metadata": {},
"outputs": [],
"source": [
"new_row = pd.DataFrame({'MAE': [mae_sarimax_month], 'R2': [r2_sarimax_month], 'RMSLE': [rmsle_sarimax_month]}, index=['Sarimax trained month data'])\n",
"# Concatenate DataFrame with new row\n",
"df_results = pd.concat([df_results, new_row])"
]
},
{
"cell_type": "markdown",
"id": "9b48ea8d-aaa4-4e20-8279-a18f9071dd7b",
"metadata": {},
"source": [
"Let's see the prediction of one model that has a low value of MAE:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "de583ebb-83af-4e58-99b0-c8a740a0688d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_results_specific_sarimax_model(store_nbr, family):\n",
" model_tmp = models_sarimax_month[str(store_nbr) + '_' + family]\n",
" df_test_tmp = df_test[(df_test.store_nbr == store_nbr) & (df_test.family == family)]\n",
" forecast_tmp = model_tmp.get_forecast(steps=15, exog=df_test_tmp[['onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week']])\n",
" future_predicted_values = forecast_tmp.predicted_mean\n",
" confidence_intervals = forecast_tmp.conf_int()\n",
" \n",
" # Plot the actual sales and predicted values\n",
" plt.plot(df_test_tmp['sales'], label = 'Actual', color = 'green')\n",
" plt.plot(future_predicted_values.index, future_predicted_values, label='Prediction')\n",
" plt.fill_between(future_predicted_values.index, \n",
" confidence_intervals['lower sales'], \n",
" confidence_intervals['upper sales'], \n",
" label='Prediction',\n",
" alpha=0.15\n",
" )\n",
" plt.title('Forecast vs Actual Values')\n",
" plt.legend()\n",
" plt.show()\n",
" \n",
" print('\\nDiagnostics of model:\\n')\n",
" model_tmp.plot_diagnostics(figsize=(12, 8))\n",
" plt.show()\n",
"\n",
"store_nbr = 28\n",
"family = 'HOME AND KITCHEN II'\n",
"plot_results_specific_sarimax_model(store_nbr, family)"
]
},
{
"cell_type": "markdown",
"id": "bd67fdd2-afe8-45db-8f85-9ab117c85181",
"metadata": {},
"source": [
"From this prediction, we can say the following:\n",
"- Although the prediction is not very accurate, is quite similar and all actual values lie on the confidence interval of 95%, except for one.\n",
"- For the plot of the residuals, no pattern is visible and this is a good thing because otherwise means that model may not capture all the information in the data.\n",
"- For the Q-Q plot doesn't seem to have significant deviations from the straight line, except in the tails of the distribution, which may indicate non-normality in the residuals. This may indicate the need for model refinement or alternative modeling approaches.\n",
"- For the Correlogram, all bars fall within the shaded confidence interval. This is a good point, because otherwise it would suggest that the model may have missed some temporal dependencies in the data.\n",
"\n",
"\n",
"Let's see the prediction of the model with the highest value of MAE:"
]
},
{
"cell_type": "code",
"execution_count": 222,
"id": "37c6ecaa-32f9-46a4-863a-1909e5fa60a4",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"store_nbr = 39\n",
"family = 'CLEANING'\n",
"plot_results_specific_sarimax_model(store_nbr, family)"
]
},
{
"cell_type": "markdown",
"id": "1760bc2e-38b5-4259-9015-0ce0feb5a615",
"metadata": {},
"source": [
"For this model we can same the same from the other model, but for this one is even worse the forecasting compared with the actual values, and for the Q-Q plot shows more deviations from the straight line."
]
},
{
"cell_type": "markdown",
"id": "cf2970fb-1062-418a-85c4-aae8f5aed0c7",
"metadata": {},
"source": [
"\n",
"#### Evaluation for test set Kaggle"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90f4a2b7-d735-484f-ad06-b90e1bb36052",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"store_list = a_final_eval.store_nbr.sort_values().unique()\n",
"family_group = a_final_eval.family.sort_values().unique()\n",
"kaggle_results = pd.DataFrame()\n",
"exog_features = ['onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week']\n",
"df_train_tmp = train_df[train_df.date >= '2017-08-01']\n",
"i = 0\n",
"for store_nbr in store_list:\n",
" for family in family_group:\n",
" model = str(store_nbr) + \"_\" + family\n",
" df_train_tmp_2 = df_train_tmp[(df_train_tmp.store_nbr == store_nbr) & (df_train_tmp.family == family)]\n",
" df_test_tmp = test_df[(test_df.store_nbr == store_nbr) & (test_df.family == family)]\n",
" df_august = pd.concat([df_train_tmp_2,df_test_tmp]) #Need to have historical data to get predictions\n",
" forecast = models_sarimax_month[model].get_prediction(start=pd.to_datetime('2017-08-16').date(),end= pd.to_datetime('2017-08-31').date(), exog=df_august[exog_features])\n",
" tmp_results = df_test_tmp.merge(forecast.predicted_mean, left_on = 'date', right_index = True, how = 'left')[['id','predicted_mean']]\n",
" kaggle_results = pd.concat([kaggle_results, tmp_results])\n",
" # counter of iterations\n",
" i+=1\n",
" if i%100 == 0:\n",
" print(i)\n",
"\n",
"kaggle_results = kaggle_results.rename(columns={\"predicted_mean\": \"sales\"}).sort_values(by='id')\n",
"today = str(date.today())\n",
"kaggle_results.to_csv('submission_sarimax_{date}.csv'.format(date=today), index=False) \n",
"print(kaggle_results.head())"
]
},
{
"cell_type": "markdown",
"id": "b3d44293-59d8-48f8-aeb3-5895d34b5e67",
"metadata": {},
"source": [
"\n",
"### Prophet by Facebook \n",
"\n",
"Prophet could be a good fit for our data because this model takes into account:\n",
"- **Trends**: Automatically detects linear or non-linear trends.\n",
"- **Seasonality**: Daily, weekly, or yearly recurring patterns are automatically identified.\n",
"- **Holidays**: You can explicitly define holidays or use a method for taking into account the holidays of specific country.\n",
"- **External features**: Can be added into the model as external 'regressors'.\n",
" \n",
"Prophet is designed for **ease of use**. You simply provide your historical data with timestamps and (optionally) holiday information. Check [here](https://medium.com/illumination/understanding-fb-prophet-a-time-series-forecasting-algorithm-c998bc52ca10) for more information about the prophet model.\n",
"\n",
"For hardware and time performance limitations, data must be partitioned and trained a separately different sarimax models for every partition. These partitions will be done with every combination of store number and family product.\n",
"\n",
"In the next cell, you can see a traning and a scoring function. In the training function a prophet model is trained for each combination of store and family product. For the scoring function, MAE, R2 and RMSLE are calculated for each model and then a general MAE,R2 and RMSLE are calculated.\n",
"\n",
"At first, we'll try to fit data with a monthly historical range and then we'll use the year of historical dataframe."
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "0bf8c6d7-335c-47f2-b9ad-5ba4377bbd0f",
"metadata": {},
"outputs": [],
"source": [
"def train_multiple_prophet_models(df_train, file_path):\n",
" models_prophet = {}\n",
" # Transform dataset to meet prophet model requirements \n",
" prophet_df = df_train[['date','store_nbr','family','sales','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week','day_of_month','month','week']].rename(columns={\"date\": \"ds\",\"sales\":\"y\"})\n",
" \n",
" store_list = prophet_df.store_nbr.sort_values().unique()\n",
" family_group = prophet_df.family.sort_values().unique()\n",
" # For each combination of store and family, train a model\n",
" for store in store_list:\n",
" for family in family_group:\n",
" model_name = str(store) + \"_\" + family\n",
" df_train_tmp = prophet_df[(prophet_df.store_nbr == store) & (prophet_df.family == family)]\n",
" model = Prophet(interval_width=0.95, weekly_seasonality=True)\n",
" model.add_country_holidays(country_name='EC')\n",
" model.add_regressor('onpromotion')\n",
" model.add_regressor('dcoilwtico')\n",
" model.add_regressor('is_local_hol_or_eve')\n",
" model.add_regressor('is_regional_hol_or_eve')\n",
" model.add_regressor('is_national_hol_or_eve')\n",
" model.add_regressor('day_of_week')\n",
" model.add_regressor('day_of_month')\n",
" model.add_regressor('month')\n",
" model.add_regressor('week')\n",
"\n",
" model.fit(df_train_tmp)\n",
" models_prophet[model_name] = model\n",
"\n",
" # Open the file in binary mode\n",
" with open(file_path, 'wb') as file:\n",
" # Serialize and write the variable to the file\n",
" pickle.dump(models_prophet, file)\n",
"\n",
" return models_prophet \n",
"\n",
"def prophet_metric_errors(models_prophet, df_test):\n",
" # Initializate lists to store results\n",
" forecast_values = [] \n",
" real_values = [] \n",
" r2_values = []\n",
" mae_values = []\n",
" models_name = []\n",
" rmsle_values = []\n",
"\n",
" # Prepate dataset for prophet model\n",
" prophet_test_df = df_test[['date', 'store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week','day_of_month','month','week']].rename(columns={\"date\": \"ds\"})\n",
" \n",
" store_list = prophet_test_df.store_nbr.sort_values().unique()\n",
" family_group = prophet_test_df.family.sort_values().unique()\n",
" \n",
" for store in store_list:\n",
" for family in family_group:\n",
" model = str(store) + \"_\" + family\n",
" if model in models_prophet.keys():\n",
" df_test_tmp = prophet_test_df[(prophet_test_df.store_nbr == store) & (prophet_test_df.family == family)]\n",
" forecast = models_prophet[model].predict(df_test_tmp.drop(columns=['sales','family','store_nbr']))\n",
" actual_values = df_test_tmp['sales'].values\n",
" future_predicted_values = forecast['yhat'].values\n",
" # Append all forecast and real values for general metric errors\n",
" forecast_values.extend(future_predicted_values)\n",
" real_values.extend(actual_values)\n",
" \n",
" # Calculate error metrics for each specific model\n",
" models_name.append(model)\n",
" r2_scr = r2_score(actual_values,future_predicted_values)\n",
" # Because r2 is not lower bounded, for visualizig purposes set 0 all those negative r2 scores.\n",
" if r2_scr <= 0:\n",
" r2_scr = 0\n",
" r2_values.append(r2_scr)\n",
" mae_values.append(mean_absolute_error(actual_values,future_predicted_values))\n",
" future_predicted_values = [0 if i < 0 else i for i in future_predicted_values] #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle_values.append(sqrt(mean_squared_log_error(actual_values,future_predicted_values)))\n",
" \n",
" data = {\n",
" 'r2': r2_values,\n",
" 'mae': mae_values,\n",
" 'rmsle': rmsle_values\n",
" }\n",
" df = pd.DataFrame(data, index=models_name)\n",
" # create histogram with a different y scale for different rows\n",
" selection = ['r2', 'mae','rmsle']\n",
" fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14,4))\n",
" for i, col in enumerate(selection):\n",
" ax = sns.histplot(data=df, x=col, ax=axes.flatten()[i], bins = 50)\n",
" ax.set_ylabel('value')\n",
" ax.set_xlabel(col)\n",
" plt.show()\n",
" \n",
" # General metrics\n",
" r2 = r2_score(real_values,forecast_values)\n",
" print(\"\\nGeneral value for R2 metric for all models is: \" + str(r2))\n",
" mae = mean_absolute_error(real_values,forecast_values)\n",
" print(\"\\nGeneral value for MAE metric for all models is:\" + str(mae))\n",
" forecast_values = [0 if i < 0 else i for i in forecast_values] #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle = sqrt(mean_squared_log_error(real_values,forecast_values))\n",
" print(\"\\nGeneral value for RMSLE metric for all models is:\" + str(rmsle))\n",
" \n",
" return r2, mae, rmsle, df"
]
},
{
"cell_type": "markdown",
"id": "570a8220-90c0-4c8a-88e2-58b2a335049a",
"metadata": {},
"source": [
"\n",
"#### Monthly historical data\n",
"\n",
"Train the model or load trained model from disk."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "345af59f-75ef-4669-ac40-aff2b20478d5",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 3.16 s\n",
"Wall time: 3.36 s\n"
]
}
],
"source": [
"%%time \n",
"#18min\n",
"file_path = 'prophet_month_trained.pickle'\n",
"# models_prophet_month = train_multiple_prophet_models(df_train_month, file_path)\n",
"with open(file_path, 'rb') as file:\n",
" # Deserialize and retrieve the variable from the file\n",
" models_prophet_month = pickle.load(file)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "64ce08ac-5f62-43e1-b414-e8434e7eefdd",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"General value for R2 metric for all models is: 0.8816669327734493\n",
"\n",
"General value for MAE metric for all models is:118.25362231809567\n",
"\n",
"General value for RMSLE metric for all models is:0.6792065122691611\n",
"CPU times: total: 3min 33s\n",
"Wall time: 3min 33s\n"
]
}
],
"source": [
"%%time \n",
"r2_prophet_month, mae_prophet_month, rmsle_prophet_month, all_models_metric_prophet_month = prophet_metric_errors(models_prophet_month, df_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f5f1947a-6d63-4536-b8b4-a30c1f69cd95",
"metadata": {},
"outputs": [],
"source": [
"new_row = pd.DataFrame({'MAE': [mae_prophet_month], 'R2': [r2_prophet_month], 'RMSLE': [rmsle_prophet_month]}, index=['Prophet trained month data'])\n",
"df_results = pd.concat([df_results, new_row])"
]
},
{
"cell_type": "markdown",
"id": "a273d91e-48c8-40d2-b388-91b9d31c4edc",
"metadata": {},
"source": [
"Let's check the prediction of the same models plot in the Sarimax section:"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "6b3e7c81-f80e-4209-b84f-5503386e72f5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"store = 39\n",
"family = 'CLEANING'\n",
"plot_prophet_forecast_vs_actual_values(df_test, models_prophet_month, store, family)"
]
},
{
"cell_type": "markdown",
"id": "977d2dcd-126e-423b-b66c-eb812f112b20",
"metadata": {},
"source": [
"This model is performing way worst than the sarimax forecast."
]
},
{
"cell_type": "markdown",
"id": "28f77a23-c8a3-4da2-95c4-ed0f465cf436",
"metadata": {},
"source": [
"\n",
"#### Year historical data\n",
"\n",
"Train the model or load trained model from disk."
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6c0ff933-baed-4057-a66b-56a81fc6cfce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 12min 4s\n",
"Wall time: 19min 17s\n"
]
}
],
"source": [
"%%time \n",
"#19 min\n",
"file_path = 'prophet_year_trained2.pickle'\n",
"# models_prophet_year = train_multiple_prophet_models(df_train_year, file_path)\n",
"with open(file_path, 'rb') as file:\n",
" # Deserialize and retrieve the variable from the file\n",
" models_prophet_year = pickle.load(file)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "912a0850-0cf3-4fc4-8e42-23ca5ad3512b",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"General value for R2 metric for all models is: 0.9415668276669995\n",
"\n",
"General value for MAE metric for all models is:90.74235849878538\n",
"\n",
"General value for RMSLE metric for all models is:0.4886493013308016\n",
"CPU times: total: 4min 13s\n",
"Wall time: 4min 16s\n"
]
}
],
"source": [
"%%time\n",
"r2_prophet_year, mae_prophet_year, rmsle_prophet_year, all_models_metric_prophet_year = prophet_metric_errors(models_prophet_year, df_test)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "55425025-bb75-4348-8c13-4b2d8a7f583b",
"metadata": {},
"outputs": [],
"source": [
"new_row = pd.DataFrame({'MAE': [mae_prophet_year], 'R2': [r2_prophet_year], 'RMSLE': [rmsle_prophet_year]}, index=['Prophet2 trained year data'])\n",
"df_results = pd.concat([df_results, new_row])"
]
},
{
"cell_type": "markdown",
"id": "dfe26d00-b441-4263-9152-de57f0f23ebe",
"metadata": {},
"source": [
"As you can see, we get the **best results so far**. This also can be seen in the graphs below, which are the same time series plots than in sarimax and in monthly prophet version. With this model, the forecasting is more similar to actual values than in the other models. "
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "56cd34d1-b90e-49bb-a08e-d754830598b6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"store = 39\n",
"family = 'CLEANING'\n",
"plot_prophet_forecast_vs_actual_values(df_test, models_prophet_year, store, family)"
]
},
{
"cell_type": "markdown",
"id": "7183e2cf-5506-47ce-93a8-dcc76cae22a6",
"metadata": {},
"source": [
"\n",
"#### Evaluation for test set Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "6c824050-79ab-4839-bd05-36c83e1cd246",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 3min 57s\n",
"Wall time: 3min 59s\n"
]
}
],
"source": [
"%%time\n",
"y_prediction = []\n",
"ids = []\n",
"a_final_eval = test_df.rename(columns={\"date\": \"ds\"})\n",
"\n",
"store_list = a_final_eval.store_nbr.sort_values().unique()\n",
"family_group = a_final_eval.family.sort_values().unique()\n",
"\n",
"for store in store_list:\n",
" for family in family_group:\n",
" tmp = a_final_eval[(a_final_eval.store_nbr == store) & (a_final_eval.family == family)]\n",
" if tmp.size > 0:\n",
" model = str(store) + \"_\" + family\n",
" forecast = models_prophet_year[model].predict(tmp)\n",
" y_prediction.extend(forecast['yhat'].values)\n",
" ids.extend(tmp.id.values)\n",
"\n",
"kaggle_results = pd.DataFrame({'id': ids, 'sales': y_prediction}).sort_values(by='id')\n",
"kaggle_results[kaggle_results.sales < 0]['sales'] = 0 # Negative results don't make sense in terms of sales\n",
"from datetime import date\n",
"today = str(date.today())\n",
"kaggle_results.to_csv('submission_prophet_{date}.csv'.format(date = today), index=False) "
]
},
{
"cell_type": "markdown",
"id": "e1b84472-02bc-4424-9fe3-f5d7c026c74d",
"metadata": {},
"source": [
"\n",
"### XGBoost\n",
"\n",
"ML models could be a good fit for our data because also can take into account seasonality components and also exogenous (i.e. external) variables can be added to the model.\n",
"\n",
"Xgboost model is a type of model made of **multiple decision trees**, actually is an ensemble of two types of model: a weak one and strong one. This model is made in an iterative way: in each step the weak model (which is a tree) is trained to predict the error of the strong one and then this weak model is added to the strong one to reduce its the error in each iteration.\n",
"\n",
"In tree algorithms, the value of the feature does not matter, but the order of the values, that's why in these models the standardization of the data is not necessary and in this section we won't standardize the data. They are robust to noisy data, have interpretable properties and are well suited for training on small datasets, or on datasets where the ratio of number of features / number of examples is high.\n",
"\n",
"This model can be used for forecasting. The core idea is to leverage past observations to predict future values. You can add **lagged versions of your target variable as features**. For example, to predict sales next day, you can include features like sales from the previous days.\n",
"\n",
"To train the xgboost model, what I've done is the following:\n",
"1. First, train the basic xgboost (with cross validation) with different lagged values and get the number of lagged values that lead the best error metrics.\n",
"2. Secondly, train with cross validation and hyperparameter tunning the model with the best metric error from the previous step.\n",
"\n",
"\n",
"For the scoring function, MAE, RMSLE and R2 is calculated for each model and then a general MAE, RMSLE and R2 is calculated.\n",
"\n",
"At first, we'll try to fit data with a monthly historical range and then we'll use the year of historical dataframe."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f9f4d36d-a321-4f17-9b1b-90c6bea1a310",
"metadata": {},
"outputs": [],
"source": [
"# Feature engineering to add lagged values for the sales feature in the dataset\n",
"\n",
"def create_lag_features(group,total_lags):\n",
" for lag in range(1,total_lags+1):\n",
" key = 'sales_lag_' + str(lag)\n",
" group[key] = group['sales'].shift(lag)\n",
" return group\n",
"\n",
"def create_lag_values_sales(df,total_lags, features = ['date','store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week','day_of_month','month','week']):\n",
" df_tmp = df.copy()\n",
" df_tmp = df_tmp[features]\n",
"\n",
" # Sort the DataFrame by date to ensure proper alignment\n",
" df_tmp.sort_values(by=['store_nbr', 'family', 'date'], inplace=True)\n",
"\n",
" # Apply the function to each group formed by 'family' and 'store_nbr'\n",
" df_lagged = df_tmp.groupby(['family', 'store_nbr']).apply(create_lag_features, total_lags = total_lags)\n",
" \n",
" # Drop rows with NaN resulting from lagged values\n",
" df_lagged.dropna(inplace=True)\n",
"\n",
" df_lagged.drop(columns=['date'], inplace=True)\n",
" return df_lagged\n",
" \n",
"def custom_preprocessing(df, total_lags, features = ['date','store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week','day_of_month','month','week']):\n",
" df_tmp = df.copy()\n",
" return pd.get_dummies(create_lag_values_sales(df_tmp,total_lags,features), columns=['family'])\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7f154f0e-7183-47b5-b8ed-3d9282a51b61",
"metadata": {},
"outputs": [],
"source": [
"# Get the best model with differents number of lagged values \n",
"\n",
"def metrics_error_for_different_lagged_values_xgb(df_train, df_test, features = ['date','store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week','day_of_month','month','week']):\n",
" # Create dt to store results\n",
" grid_search_lagged_values_xgb = pd.DataFrame()\n",
" testing_values_xgb = pd.DataFrame()\n",
"\n",
" # Prepare train data\n",
" df_train_xgboost = df_train.copy()\n",
" df_train_xgboost.index = df_train_xgboost.id\n",
" df_train_xgboost.drop(columns = ['id'], inplace = True)\n",
" # Prepare test data\n",
" df_test_xgboost = df_test.copy()\n",
" df_test_xgboost.index = df_test_xgboost.id\n",
" df_test_xgboost.drop(columns = ['id'], inplace = True)\n",
" \n",
" for total_lagged_values in [1,7,14,21,28,30]:\n",
" \n",
" ## Define RMSLE custom scorer\n",
" def rmsle(y_true, y_pred):\n",
" return np.sqrt(np.mean(np.square(np.log1p(y_pred) - np.log1p(y_true))))\n",
" \n",
" ## Tranform training and testing data\n",
" df_train_xgboost_tmp = custom_preprocessing(df_train_xgboost, total_lagged_values, features)\n",
" df_train_xgboost_tmp_X = df_train_xgboost_tmp.drop(columns=['sales'])\n",
" df_train_xgboost_tmp_y = df_train_xgboost_tmp['sales']\n",
" df_test_tmp = pd.concat([df_train_xgboost,df_test_xgboost])\n",
" df_test_tmp = custom_preprocessing(df_test_tmp, total_lagged_values, features)\n",
" df_test_xgboost_tmp = df_test_tmp[df_test_tmp.index.isin(df_test_xgboost.index)]\n",
" df_test_dt_X = df_test_xgboost_tmp.drop(columns=['sales'])\n",
" df_test_dt_y = df_test_xgboost_tmp['sales']\n",
" \n",
" ## Define the model and the cross validation settings\n",
" xgb_model = xgb.XGBRegressor(objective='reg:squaredlogerror')\n",
" # Define the number of folds for cross-validation\n",
" n_folds = 5\n",
" # Define custom scoring functions for different metrics\n",
" scoring = {\n",
" 'mae': make_scorer(mean_absolute_error),\n",
" 'r2': make_scorer(r2_score),\n",
" 'rmsle': make_scorer(rmsle, greater_is_better=False)\n",
" }\n",
" # Perform KFold cross-validation\n",
" kf = KFold(n_splits=n_folds, shuffle=True, random_state=42)\n",
" # Perform cross-validation and calculate multiple metrics\n",
" mae_scores = cross_val_score(xgb_model, df_train_xgboost_tmp_X, df_train_xgboost_tmp_y, scoring=scoring['mae'], cv=kf)\n",
" r2_scores = cross_val_score(xgb_model, df_train_xgboost_tmp_X, df_train_xgboost_tmp_y, scoring=scoring['r2'], cv=kf)\n",
" # For RMSLE, we need to use custom scoring and handle predictions with log transformation\n",
" rmsle_scores = cross_val_score(xgb_model, df_train_xgboost_tmp_X, df_train_xgboost_tmp_y, scoring=scoring['rmsle'], cv=kf)\n",
" rmsle_scores = -rmsle_scores # Convert negative scores to positive (RMSLE should be minimized)\n",
" # Get the mean of all cross validations sets\n",
" mae = np.mean(mae_scores)\n",
" r2 = np.mean(r2_scores)\n",
" rmsle = np.mean(rmsle_scores)\n",
" # Save cross validating score\n",
" new_row = pd.DataFrame({'MAE': [mae], 'R2': [r2], 'RMSLE': [rmsle]}, index=['XGBoost trained month data with {} lagged values'.format(total_lagged_values)])\n",
" grid_search_lagged_values_xgb = pd.concat([grid_search_lagged_values_xgb, new_row])\n",
" \n",
" ## Train model and apply it to test data\n",
" xgb_model.fit(df_train_xgboost_tmp_X, df_train_xgboost_tmp_y)\n",
" pred_xgb = xgb_model.predict(df_test_dt_X)\n",
" # Get testing score\n",
" mae = mean_absolute_error(df_test_dt_y, pred_xgb)\n",
" r2 = r2_score(df_test_dt_y, pred_xgb)\n",
" pred_xgb = [0 if i < 0 else i for i in pred_xgb] #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle = sqrt(mean_squared_log_error(df_test_dt_y, pred_xgb))\n",
" # Save testing score\n",
" new_row = pd.DataFrame({'MAE': [mae], 'R2': [r2], 'RMSLE': [rmsle]}, index=['XGBoost trained month data with {} lagged values'.format(total_lagged_values)])\n",
" testing_values_xgb = pd.concat([testing_values_xgb, new_row])\n",
"\n",
" return grid_search_lagged_values_xgb, testing_values_xgb\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "97d05a93-4158-4752-a749-50b2390daf40",
"metadata": {},
"outputs": [],
"source": [
"#Train xgboost model for forecasting\n",
"\n",
"def tune_hyperparameters_xgb_with_specific_lagged_values(df_train, df_test, number_of_lagged_values, features = ['date','store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week','day_of_month','month','week']):\n",
" # Prepate data\n",
" df_train_xgb = df_train.copy()\n",
" df_train_xgb.index = df_train_xgb.id\n",
" df_test_xgb = df_test.copy()\n",
" df_test_xgb.index = df_test_xgb.id\n",
" # Apply feature engineering\n",
" df_train_xgb = pd.get_dummies(create_lag_values_sales(df_train_xgb,number_of_lagged_values), columns=['family'])\n",
" df_test_xgb = pd.get_dummies(create_lag_values_sales(df_test_xgb,number_of_lagged_values), columns=['family'])\n",
" # Split target and features\n",
" df_train_xgb_X = df_train_xgb.drop(columns=['sales'])\n",
" df_train_xgb_y = df_train_xgb['sales']\n",
" df_test_xgb_X = df_test_xgb.drop(columns=['sales'])\n",
" df_test_xgb_y = df_test_xgb['sales'] \n",
"\n",
" # Define grid search and cross validation settings\n",
" kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
" params_grid_xgb = {\n",
" 'n_estimators' : [25,50,100],\n",
" 'max_depth': [2,3,4,5,6,7], #Control overfitting\n",
" 'colsample_bytree': [0.25, 0.5, 1], #Control overfitting\n",
" 'min_child_weight': [1,10,100], #Control overfitting\n",
" 'gamma': [1,10,100] #Control overfitting\n",
" }\n",
"# {'colsample_bytree': 1, 'gamma': 1, 'max_depth': 7, 'min_child_weight': 10, 'n_estimators': 100} a year\n",
"# {'colsample_bytree': 1, 'gamma': 1, 'max_depth': 4, 'min_child_weight': 10, 'n_estimators': 100} month\n",
" \n",
" xgb = XGBRegressor(n_jobs= -1)\n",
" # Fit xgb\n",
" grid_xgb = GridSearchCV(xgb, params_grid_xgb, n_jobs=-1, cv=kf, verbose=1, scoring=['neg_mean_squared_error'], refit='neg_mean_squared_error')\n",
" grid_xgb.fit(df_train_xgb_X,df_train_xgb_y)\n",
" print(\"Best params:\\n\")\n",
" print(grid_xgb.best_params_)\n",
" # Predict and get eval metrics\n",
" pred_grid_pipeline_xgb = grid_xgb.predict(df_test_xgb_X)\n",
" mae = mean_absolute_error(df_test_xgb_y, pred_grid_pipeline_xgb)\n",
" r2 = r2_score(df_test_xgb_y, pred_grid_pipeline_xgb)\n",
" pred_grid_pipeline_xgb = [0 if i < 0 else i for i in pred_grid_pipeline_xgb] #Mean Squared Logarithmic Error cannot be used when targets contain negative values.\n",
" rmsle = sqrt(mean_squared_log_error(df_test_xgb_y, pred_grid_pipeline_xgb))\n",
"\n",
" return grid_xgb, r2, mae, rmsle "
]
},
{
"cell_type": "markdown",
"id": "9caef213-38c1-4f04-8342-4b1708e2882a",
"metadata": {},
"source": [
"\n",
"#### Monthly historical data\n",
"\n",
"For training Xboost, let's find out how many lagged values should we add to our data. So, at first we'll search for the best base model with different combinations of lagged values, and then we'll improve that model by tunning the hyperparameters."
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "fba450cd-ae72-4f12-bb8d-dde2d2425b8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 0 ns\n",
"Wall time: 0 ns\n"
]
}
],
"source": [
"%%time \n",
"features = ['date','store_nbr','family','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','sales','day_of_week']\n",
"cross_validation_errors, testing_errors = metrics_error_for_different_lagged_values_xgb(df_train_month, df_test, features)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "b4d950a0-8eac-4643-a372-e3a129ce0cf2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
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MAE
\n",
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R2
\n",
"
RMSLE
\n",
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\n",
" \n",
" \n",
"
\n",
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XGBoost trained month data with 1 lagged values
\n",
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379.926313
\n",
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0.045549
\n",
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0.877013
\n",
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\n",
"
\n",
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XGBoost trained month data with 7 lagged values
\n",
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377.266000
\n",
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0.043180
\n",
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0.865436
\n",
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\n",
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\n",
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XGBoost trained month data with 14 lagged values
\n",
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388.200446
\n",
"
0.027624
\n",
"
0.900591
\n",
"
\n",
"
\n",
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XGBoost trained month data with 21 lagged values
\n",
"
409.465604
\n",
"
-0.003164
\n",
"
0.996473
\n",
"
\n",
"
\n",
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XGBoost trained month data with 28 lagged values
\n",
"
503.715511
\n",
"
-0.051668
\n",
"
1.232517
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 30 lagged values
\n",
"
452.422905
\n",
"
-0.079092
\n",
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1.392749
\n",
"
\n",
" \n",
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\n",
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"
],
"text/plain": [
" MAE R2 \\\n",
"XGBoost trained month data with 1 lagged values 379.926313 0.045549 \n",
"XGBoost trained month data with 7 lagged values 377.266000 0.043180 \n",
"XGBoost trained month data with 14 lagged values 388.200446 0.027624 \n",
"XGBoost trained month data with 21 lagged values 409.465604 -0.003164 \n",
"XGBoost trained month data with 28 lagged values 503.715511 -0.051668 \n",
"XGBoost trained month data with 30 lagged values 452.422905 -0.079092 \n",
"\n",
" RMSLE \n",
"XGBoost trained month data with 1 lagged values 0.877013 \n",
"XGBoost trained month data with 7 lagged values 0.865436 \n",
"XGBoost trained month data with 14 lagged values 0.900591 \n",
"XGBoost trained month data with 21 lagged values 0.996473 \n",
"XGBoost trained month data with 28 lagged values 1.232517 \n",
"XGBoost trained month data with 30 lagged values 1.392749 "
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_validation_errors"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "014bab3b-9ca8-4508-abd4-7c122df15c8d",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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\n",
"\n",
"
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" \n",
"
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MAE
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R2
\n",
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RMSLE
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"
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" \n",
" \n",
"
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XGBoost trained month data with 1 lagged values
\n",
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358.867476
\n",
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0.068387
\n",
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0.840346
\n",
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\n",
"
\n",
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XGBoost trained month data with 7 lagged values
\n",
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358.043647
\n",
"
0.065853
\n",
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0.822166
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\n",
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XGBoost trained month data with 14 lagged values
\n",
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367.868547
\n",
"
0.044603
\n",
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0.875653
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 21 lagged values
\n",
"
376.955882
\n",
"
0.019534
\n",
"
0.936229
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 28 lagged values
\n",
"
400.109432
\n",
"
-0.028804
\n",
"
1.137628
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 30 lagged values
\n",
"
414.914269
\n",
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-0.064671
\n",
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1.281433
\n",
"
\n",
" \n",
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\n",
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"
],
"text/plain": [
" MAE R2 \\\n",
"XGBoost trained month data with 1 lagged values 358.867476 0.068387 \n",
"XGBoost trained month data with 7 lagged values 358.043647 0.065853 \n",
"XGBoost trained month data with 14 lagged values 367.868547 0.044603 \n",
"XGBoost trained month data with 21 lagged values 376.955882 0.019534 \n",
"XGBoost trained month data with 28 lagged values 400.109432 -0.028804 \n",
"XGBoost trained month data with 30 lagged values 414.914269 -0.064671 \n",
"\n",
" RMSLE \n",
"XGBoost trained month data with 1 lagged values 0.840346 \n",
"XGBoost trained month data with 7 lagged values 0.822166 \n",
"XGBoost trained month data with 14 lagged values 0.875653 \n",
"XGBoost trained month data with 21 lagged values 0.936229 \n",
"XGBoost trained month data with 28 lagged values 1.137628 \n",
"XGBoost trained month data with 30 lagged values 1.281433 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"testing_errors"
]
},
{
"cell_type": "markdown",
"id": "d057351a-9286-48ea-80d7-5d8d200ce287",
"metadata": {},
"source": [
"As we can see in the cross validation and testing scores, we get the best error metric when we're using 7 lagged values. So, we'll tune the xgb model with 7 lagged values:\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "53284625-6ddd-4fb6-9f65-eea491ed34fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
"Best params:\n",
"\n",
"{'colsample_bytree': 1, 'gamma': 1, 'max_depth': 4, 'min_child_weight': 10, 'n_estimators': 100}\n",
"CPU times: total: 19.1 s\n",
"Wall time: 21.5 s\n"
]
}
],
"source": [
"%%time\n",
"total_lagged_values = 7\n",
"xgb_trained, r2_xgb, mae_xgb, rmsle_xgb = tune_hyperparameters_xgb_with_specific_lagged_values(df_train_month, df_test, total_lagged_values, features)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "2a35ea58-5338-430b-b134-303b2d7a887d",
"metadata": {},
"outputs": [],
"source": [
"new_row = pd.DataFrame({'MAE': [mae_xgb], 'R2': [r2_xgb], 'RMSLE': [rmsle_xgb]}, index=['XGBoost trained monthly data with {} lagged values'.format(total_lagged_values)])\n",
"df_results = pd.concat([df_results, new_row])"
]
},
{
"cell_type": "markdown",
"id": "b8e4af93-b207-411c-8292-3593cf372793",
"metadata": {},
"source": [
"\n",
"#### Year historical data\n",
"\n",
"As we've done previously, we'll find out how many lagged values as input features works the best for xgboost using training data for the last year"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "f89a992e-66db-4540-818a-8b04c816dd4b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: total: 1h 45min 10s\n",
"Wall time: 20min 54s\n"
]
}
],
"source": [
"%%time \n",
"cross_validation_errors, testing_errors = metrics_error_for_different_lagged_values_xgb(df_train_year, df_test)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "0171821b-49ec-4b23-b499-1137128e8c6c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \n",
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" \n",
" \n",
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XGBoost trained month data with 1 lagged values
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300.266802
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0.227350
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XGBoost trained month data with 7 lagged values
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293.679030
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0.262843
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0.684930
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XGBoost trained month data with 14 lagged values
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"
276.669584
\n",
"
0.270898
\n",
"
0.611705
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 21 lagged values
\n",
"
285.295382
\n",
"
0.268672
\n",
"
0.670663
\n",
"
\n",
"
\n",
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XGBoost trained month data with 28 lagged values
\n",
"
285.681328
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0.253169
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0.623490
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"
\n",
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\n",
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XGBoost trained month data with 30 lagged values
\n",
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287.399887
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0.249947
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0.626406
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" \n",
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"text/plain": [
" MAE R2 \\\n",
"XGBoost trained month data with 1 lagged values 300.266802 0.227350 \n",
"XGBoost trained month data with 7 lagged values 293.679030 0.262843 \n",
"XGBoost trained month data with 14 lagged values 276.669584 0.270898 \n",
"XGBoost trained month data with 21 lagged values 285.295382 0.268672 \n",
"XGBoost trained month data with 28 lagged values 285.681328 0.253169 \n",
"XGBoost trained month data with 30 lagged values 287.399887 0.249947 \n",
"\n",
" RMSLE \n",
"XGBoost trained month data with 1 lagged values 0.700534 \n",
"XGBoost trained month data with 7 lagged values 0.684930 \n",
"XGBoost trained month data with 14 lagged values 0.611705 \n",
"XGBoost trained month data with 21 lagged values 0.670663 \n",
"XGBoost trained month data with 28 lagged values 0.623490 \n",
"XGBoost trained month data with 30 lagged values 0.626406 "
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_validation_errors"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "7c084177-a26b-4f74-a241-b9391b33f093",
"metadata": {},
"outputs": [
{
"data": {
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XGBoost trained month data with 1 lagged values
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287.066683
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"
\n",
"
XGBoost trained month data with 7 lagged values
\n",
"
293.658663
\n",
"
0.351584
\n",
"
0.775386
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 14 lagged values
\n",
"
248.344843
\n",
"
0.400040
\n",
"
0.514936
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 21 lagged values
\n",
"
292.675255
\n",
"
0.318560
\n",
"
0.801010
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 28 lagged values
\n",
"
292.462246
\n",
"
0.239424
\n",
"
0.592876
\n",
"
\n",
"
\n",
"
XGBoost trained month data with 30 lagged values
\n",
"
281.620008
\n",
"
0.287207
\n",
"
0.565661
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" MAE R2 \\\n",
"XGBoost trained month data with 1 lagged values 287.066683 0.357177 \n",
"XGBoost trained month data with 7 lagged values 293.658663 0.351584 \n",
"XGBoost trained month data with 14 lagged values 248.344843 0.400040 \n",
"XGBoost trained month data with 21 lagged values 292.675255 0.318560 \n",
"XGBoost trained month data with 28 lagged values 292.462246 0.239424 \n",
"XGBoost trained month data with 30 lagged values 281.620008 0.287207 \n",
"\n",
" RMSLE \n",
"XGBoost trained month data with 1 lagged values 0.671237 \n",
"XGBoost trained month data with 7 lagged values 0.775386 \n",
"XGBoost trained month data with 14 lagged values 0.514936 \n",
"XGBoost trained month data with 21 lagged values 0.801010 \n",
"XGBoost trained month data with 28 lagged values 0.592876 \n",
"XGBoost trained month data with 30 lagged values 0.565661 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"testing_errors"
]
},
{
"cell_type": "markdown",
"id": "f3623a1b-5800-4916-a0b4-f78dbf8a3fc9",
"metadata": {},
"source": [
"As we can see in the cross validation and testing scores, we get the best error metric when we're using 14 lagged values. So, we'll tune the xgb model with 14 lagged values:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "5ab4841c-510a-4882-b46b-2c555bf28fa7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
"Best params:\n",
"\n",
"{'colsample_bytree': 1, 'gamma': 1, 'max_depth': 7, 'min_child_weight': 10, 'n_estimators': 100}\n",
"CPU times: total: 1min 14s\n",
"Wall time: 1min 6s\n"
]
}
],
"source": [
"%%time\n",
"total_lagged_values = 14\n",
"xgb_trained_year, r2_xgb, mae_xgb, rmsle_xgb = tune_hyperparameters_xgb_with_specific_lagged_values(df_train_year, df_test, total_lagged_values)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "3bbe0514-f996-4b29-a88b-b31e8eb96084",
"metadata": {},
"outputs": [],
"source": [
"new_row = pd.DataFrame({'MAE': [mae_xgb], 'R2': [r2_xgb], 'RMSLE': [rmsle_xgb]}, index=['XGBoost trained year data with {} lagged values'.format(total_lagged_values)])\n",
"df_results = pd.concat([df_results, new_row])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "bb0a7b5f-2665-42a5-8a71-fe45d09b9254",
"metadata": {},
"outputs": [],
"source": [
"# save the year xgb model as a pickle file\n",
"xgb_year_file = \"xgb_year.pickle\" \n",
"with open(xgb_year_file, 'wb') as file: \n",
" pickle.dump(xgb_trained_year, file)\n"
]
},
{
"cell_type": "markdown",
"id": "e982cf27-2d8f-498d-827e-b3d12e27d777",
"metadata": {},
"source": [
"\n",
"#### Evaluation for test set Kaggle"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "4f330116-21ed-4b8c-89f1-746826d4b853",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id sales\n",
"24948 3000888 2.461220\n",
"24949 3000889 0.970379\n",
"24950 3000890 2.857556\n",
"24951 3000891 2178.364014\n",
"24952 3000892 0.970379\n",
"CPU times: total: 3min 35s\n",
"Wall time: 3min 24s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"model = xgb_trained_year\n",
"test_df_tmp = test_df.copy()\n",
"dates_historical_forecast = test_df_tmp.date.unique()\n",
"historical_df = train_df[train_df.date >= (min(dates_historical_forecast) - np.timedelta64(total_lagged_values,'D'))]\n",
"\n",
"prediction = []\n",
"ids = []\n",
"\n",
"for date in sorted(dates_historical_forecast):\n",
" # Retrieve historical data (or forecasted) to get lagges valued for this specific date\n",
" subset_historical_df = historical_df[historical_df.date >= (date - np.timedelta64(total_lagged_values,'D'))]\n",
" \n",
" # Retrieve data from date to forecast\n",
" test_df_tmp_2 = test_df_tmp[test_df_tmp.date == date]\n",
" # Add dummy sales columns\n",
" test_df_tmp_2['sales'] = -1\n",
" \n",
" # Union subset historical dataset with dataset with date to forecast\n",
" tmp_df = pd.concat([subset_historical_df, test_df_tmp_2], ignore_index=False)\n",
" tmp_df.index = tmp_df.id\n",
" \n",
" # Create lagged values for date to forecast dataset\n",
" tmp_df = custom_preprocessing(tmp_df,total_lagged_values)\n",
"\n",
" #Make prediction\n",
" pred_xgboost = model.predict(tmp_df.drop(columns=[\"sales\"]))\n",
"\n",
" # Add forecast to historical subset \n",
" forecast_df = pd.DataFrame({'sales': pred_xgboost}, index = tmp_df.index)\n",
" test_df_tmp_2 = test_df_tmp_2.drop(columns=['sales']).merge(forecast_df, left_on='id', how='left', right_index=True)\n",
" historical_df = pd.concat([historical_df, test_df_tmp_2], ignore_index=True)\n",
"\n",
"# Save results\n",
"kaggle_results = historical_df[historical_df.date >= '2017-08-16'][['id','sales']].sort_values(by='id')\n",
"kaggle_results[kaggle_results.sales < 0]['sales'] = 0 # Negative results don't make sense in terms of sales\n",
"from datetime import date\n",
"today = str(date.today())\n",
"kaggle_results.to_csv('submission_xgb_{date}.csv'.format(date=today), index=False) \n",
"print(kaggle_results.head())"
]
},
{
"cell_type": "markdown",
"id": "3ec2798e-7e64-4602-84cb-35931b420676",
"metadata": {},
"source": [
"\n",
"\n",
"## Model comparison\n",
"As you can see in the next cell, there are all the performance results for each model we've tried to fit to our data. Our main metrics have been the MAE, R2 and RMSLE so we'll sort the results by RSMLE, which is the same metric evaluation for the Kaggle project: "
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "fdc8a11e-a13f-422a-b9b2-66e097836e64",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
MAE
\n",
"
R2
\n",
"
RMSLE
\n",
"
\n",
" \n",
" \n",
"
\n",
"
XGBoost2 trained year data with 14 lagged values
\n",
"
47.627658
\n",
"
0.978860
\n",
"
0.467023
\n",
"
\n",
"
\n",
"
Prophet trained year data
\n",
"
82.317945
\n",
"
0.947267
\n",
"
0.489487
\n",
"
\n",
"
\n",
"
Sarimax trained month data
\n",
"
106.060052
\n",
"
0.909375
\n",
"
0.621541
\n",
"
\n",
"
\n",
"
XGBoost trained monthly data with 7 lagged values
\n",
"
68.357731
\n",
"
0.959250
\n",
"
0.621748
\n",
"
\n",
"
\n",
"
Prophet trained month data
\n",
"
118.253622
\n",
"
0.881667
\n",
"
0.679207
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" MAE R2 \\\n",
"XGBoost2 trained year data with 14 lagged values 47.627658 0.978860 \n",
"Prophet trained year data 82.317945 0.947267 \n",
"Sarimax trained month data 106.060052 0.909375 \n",
"XGBoost trained monthly data with 7 lagged values 68.357731 0.959250 \n",
"Prophet trained month data 118.253622 0.881667 \n",
"\n",
" RMSLE \n",
"XGBoost2 trained year data with 14 lagged values 0.467023 \n",
"Prophet trained year data 0.489487 \n",
"Sarimax trained month data 0.621541 \n",
"XGBoost trained monthly data with 7 lagged values 0.621748 \n",
"Prophet trained month data 0.679207 "
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_results.sort_values(by = 'RMSLE')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "855fbd88-bfdc-40ac-b71f-e8ec1ee141d3",
"metadata": {},
"source": [
"As we can see above, we obtain the best results by training yearly data. Our best results are with the xgboost and prophet models. Let's submit a prediction of each of them at the kaggle competition and let's see which score we get with both of them.\n",
"\n",
"- **Results from Xgboost:**\n",
" ![results_xgboost_kaggle.png](results_xgboost_kaggle.png)\n",
" \n",
"- **Results from Prophet:**\n",
" ![results_prophet_kaggle.png](results_prophet_kaggle.png)\n",
" \n",
"As we can see above, both models performed worse than our test set. This could mean that there are overfitting in both models. On the other hand, specially the Xgboost performed worse than the prophet. This could be due to the fact that for xgboost we've used 14 lagged values and our window to forecast was about 15 days, so we've used forecasted values to predict also future values. For example, for the 15th day to predict, we haven't used any historical data, only predicted values, then, we may be carrying forward prediction errors that in turn affect the prediction of future values.\n",
"\n",
"To sum up, we can say that out **best model has been Prophet** trained with 1 year of data."
]
},
{
"cell_type": "markdown",
"id": "e460ef53-9cd3-4147-8d13-642b29c790ab",
"metadata": {},
"source": [
"\n",
"# To production\n",
"\n",
"To recap, what we have done so far is analyze the dataset of the sales of Favorita Stores, and we have created a model capable of predicting the future sales based on the features of the dataset. Now, we are going to leverage this model and truly see the value that this work will bring us.\n",
"\n",
"To do so, we'll predict the forecasting values for the future sales for every family product and store, and also we'll get the upper and the lower bound of sales with a 95% of confidence:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e199ce4b-b54f-4439-b2cd-e9d8cb0cb7cb",
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"y_prediction = []\n",
"y_lower = []\n",
"y_upper = []\n",
"ids = []\n",
"a_final_eval = test_df.rename(columns={\"date\": \"ds\"})\n",
"\n",
"store_list = a_final_eval.store_nbr.sort_values().unique()\n",
"family_group = a_final_eval.family.sort_values().unique()\n",
"\n",
"i = 0\n",
"\n",
"for store in store_list:\n",
" for family in family_group:\n",
" tmp = a_final_eval[(a_final_eval.store_nbr == store) & (a_final_eval.family == family)]\n",
" if tmp.size > 0:\n",
" model = str(store) + \"_\" + family\n",
" forecast = models_prophet_year[model].predict(tmp)\n",
" y_prediction.extend(forecast['yhat'].values)\n",
" y_lower.extend(forecast['yhat_lower'].values)\n",
" y_upper.extend(forecast['yhat_upper'].values)\n",
" ids.extend(tmp.id.values)\n",
" i+=1\n",
" if i%100 == 0:\n",
" print(i)\n",
"\n",
"kaggle_results = pd.DataFrame({'id': ids, 'sales': y_prediction, 'lower_sales': y_lower, 'upper_sales': y_upper}).sort_values(by='id')\n",
"print(kaggle_results.head())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6302515e-6c73-43af-8fd3-6e044aeeb09a",
"metadata": {},
"outputs": [],
"source": [
"looker = test_df.merge(kaggle_results, left_on='id', how='left', right_on = 'id')\n",
"looker['sales'] = looker['sales'].apply(lambda x: round(x, 3))\n",
"looker['lower_sales'] = looker['lower_sales'].apply(lambda x: round(x, 3))\n",
"looker['upper_sales'] = looker['upper_sales'].apply(lambda x: round(x, 3))\n",
"looker"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f80dfb54-44a2-4e4d-8628-4c45b2a08e50",
"metadata": {},
"outputs": [],
"source": [
"looker.to_csv('looker_report.csv', index=False) "
]
},
{
"cell_type": "markdown",
"id": "416e99b2-a4d0-408e-93eb-dab560a9541b",
"metadata": {},
"source": [
"An important part of an ML project is being able to make the trained model available for other teams or colleagues of the company to take advantage of the insights they can get of it. One technical way could be creating an API that our colleagues could make their own predictions. Another approach it could be to present the results in an interactive report and enable anyone with the link to get their own insights.\n",
"\n",
"That's why I've created an example of a [report](https://lookerstudio.google.com/s/svVPttPBqWI) that we could create with the results of the model:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "800ff637-edb3-4bd3-9214-f7f1fb3aaafb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import IFrame, HTML\n",
"ulr= 'https://lookerstudio.google.com/embed/reporting/a787fb72-8e31-49d8-978a-c9ee525fda36/page/BKLvD'\n",
"IFrame(ulr, width='100%', height=500)"
]
},
{
"cell_type": "markdown",
"id": "29456639-b89b-42f8-baef-7b101b113af2",
"metadata": {},
"source": [
"\n",
"# Future improvements\n",
"\n",
"1. Check for outliers\n",
"2. Try sarimax with anual year (problem: high training time) \n",
"3. Try deep learning model\n",
"4. Try prophet hyperparameter and cross validation (problem: high training time) "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "54c5ce22-55d3-4f6c-84bf-52c997070df5",
"metadata": {},
"outputs": [],
"source": [
"# %%time\n",
"\n",
"# # Hyperparameter and cross validation with Prophet\n",
"# from prophet.diagnostics import cross_validation\n",
"# from prophet.diagnostics import performance_metrics\n",
"# import itertools\n",
"# import numpy as np\n",
"# import pandas as pd\n",
"\n",
"# param_grid = { \n",
"# 'changepoint_prior_scale': [0.001, 0.1],\n",
"# 'seasonality_prior_scale': [0.01, 0.1],\n",
"# 'seasonality_mode': ['additive', 'multiplicative'],\n",
"# }\n",
"\n",
"# # Generate all combinations of parameters\n",
"# all_params = [dict(zip(param_grid.keys(), v)) for v in itertools.product(*param_grid.values())]\n",
"# maes = [] # Store the MAEs for each params here\n",
"\n",
"# prophet_df = df_train_year[['date','store_nbr','family','sales','onpromotion', 'dcoilwtico','is_local_hol_or_eve','is_regional_hol_or_eve','is_national_hol_or_eve','day_of_week', 'day_of_month', 'month' ,'week']].rename(columns={\"date\": \"ds\",\"sales\":\"y\"})\n",
"# df_train_tmp = prophet_df[(prophet_df.store_nbr == 15) & (prophet_df.family == 'BEVERAGES')]\n",
"\n",
"# # Use cross validation to evaluate all parameters\n",
"# for params in all_params:\n",
"# # m = Prophet(**params).fit(df) # Fit model with given params\n",
"# model = Prophet(interval_width=0.95, weekly_seasonality=True,**params)\n",
"# model.add_country_holidays(country_name='EC')\n",
"# model.add_regressor('onpromotion')\n",
"# model.add_regressor('dcoilwtico')\n",
"# model.add_regressor('is_local_hol_or_eve')\n",
"# model.add_regressor('is_regional_hol_or_eve')\n",
"# model.add_regressor('is_national_hol_or_eve')\n",
"# model.add_regressor('day_of_week')\n",
"# model.add_regressor('day_of_month')\n",
"# model.add_regressor('month')\n",
"# model.add_regressor('week')\n",
"# model.fit(df_train_tmp)\n",
" \n",
"# df_cv = cross_validation(model, horizon='30 days', parallel=\"processes\")\n",
"# df_p = performance_metrics(df_cv)\n",
"# maes.append(df_p['mae'].values[0])\n",
"\n",
"# # Find the best parameters\n",
"# tuning_results = pd.DataFrame(all_params)\n",
"# tuning_results['mae'] = maes\n",
"# print(tuning_results)"
]
}
],
"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": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}