Vapaunic/lib (#37)
* initial core for forecasting library
* syncing with new structure
* __init__ files in modules
* renamed lib directory
* Added legal headers and some formatting of py files
* restructured benchmarking directory in lib
* fixed imports, warnings, legal headers
* more import fixes and legal headers
* updated instructions with package installation
* barebones library README
* moved energy benchmark to contrib
* formatting changes plus more legal headers
* Added license to the setup
* moved .swp file to contrib, not sure we need to keep it at all
* added missing headers and a brief snipet to README file
* minor wording change in readme
Former-commit-id: a5e3b26ca8
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vapaunic
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Коммит
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# line too long
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# trailing white spaces
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**/__pycache__
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**/.ipynb_checkpoints
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*.egg-info/
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.vscode/
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data/*
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energy_load/GEFCom2017-D_Prob_MT_hourly/data/*
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@ -35,6 +35,22 @@ This will create the appropriate conda environment to run experiments. Next acti
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conda activate forecast
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```
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During development, in case you need to update the environment due to a conda env file change, you can run
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```
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conda env update --file tools/environment.yaml
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```
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from the root of Forecasting repo.
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#### Package Installation
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Next you will need to install the common package for forecasting:
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```bash
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pip install -e forecasting_lib
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```
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The library is installed in developer mode with the `-e` flag. This means that all changes made to the library locally, are immediately available.
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## Steps to Contributing
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Here are the basic steps to get started with your first contribution. Please reach out with any questions.
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|
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74
README.md
74
README.md
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@ -1,73 +1,3 @@
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# TSPerf
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TSPerf is a collection of implementations of time-series forecasting algorithms in Azure cloud and comparison of their performance over benchmark datasets. Algorithm implementations are compared by model accuracy, training and scoring time and cost. Each implementation includes all the necessary instructions and tools that ensure its reproducibility.
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The following table summarizes benchmarks that are currently included in TSPerf.
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Benchmark | Dataset | Benchmark directory
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--------------------------------------------|------------------------|---------------------------------------------
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Probabilistic electricity load forecasting | GEFCom2017 | `energy_load/GEFCom2017-D_Prob_MT_Hourly`
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Retail sales forecasting | Orange Juice dataset | `retail_sales/OrangeJuice_Pt_3Weeks_Weekly`
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A complete documentation of TSPerf, along with the instructions for submitting and reviewing implementations, can be found [here](./docs/tsperf_rules.md). The tables below show performance of implementations that are developed so far. Source code of implementations and instructions for reproducing their performance can be found in submission folders, which are linked in the first column.
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## Probabilistic energy forecasting performance board
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The following table lists the current submision for the energy forecasting and their respective performances.
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Submission Name | Pinball Loss | Training and Scoring Time (sec) | Training and Scoring Cost($) | Architecture | Framework | Algorithm | Uni/Multivariate | External Feature Support
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-----------------------------------------------------------------|----------------|-----------------------------------|--------------------------------|-----------------------------------------------|------------------------------------|---------------------------------------|--------------------|--------------------------
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[Baseline](benchmarks%2FGEFCom2017_D_Prob_MT_hourly%2Fbaseline) | 84.12 | 188 | 0.0201 | Linux DSVM (Standard D8s v3 - Premium SSD) | quantreg package of R | Linear Quantile Regression | Multivariate | Yes
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[GBM](benchmarks%2FGEFCom2017_D_Prob_MT_hourly%2FGBM) | 78.84 | 269 | 0.0287 | Linux DSVM (Standard D8s v3 - Premium SSD) | gbm package of R | Gradient Boosting Decision Tree | Multivariate | Yes
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[QRF](benchmarks%2FGEFCom2017_D_Prob_MT_hourly%2Fqrf) | 76.29 | 20322 | 17.19 | Linux DSVM (F72s v2 - Premium SSD) | scikit-garden package of Python | Quantile Regression Forest | Multivariate | Yes
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[FNN](benchmarks%2FGEFCom2017_D_Prob_MT_hourly%2Ffnn) | 80.06 | 1085 | 0.1157 | Linux DSVM (Standard D8s v3 - Premium SSD) | qrnn package of R | Quantile Regression Neural Network | Multivariate | Yes
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| | | | | | | |
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The following chart compares the submissions performance on accuracy in Pinball Loss vs. Training and Scoring cost in $:
|
||||
|
||||
|
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|
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## Retail sales forecasting performance board
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The following table lists the current submision for the retail forecasting and their respective performances.
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Submission Name | MAPE (%) | Training and Scoring Time (sec) | Training and Scoring Cost ($) | Architecture | Framework | Algorithm | Uni/Multivariate | External Feature Support
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----------------------------------------------------------------------------|------------|-----------------------------------|---------------------------------|----------------------------------------------|------------------------------|---------------------------------------------------------------------|--------------------|--------------------------
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[Baseline](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2Fbaseline) | 109.67 | 114.06 | 0.003 | Linux DSVM(Standard D2s v3 - Premium SSD) | forecast package of R | Naive Forecast | Univariate | No
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[AutoARIMA](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FARIMA) | 70.80 | 265.94 | 0.0071 | Linux DSVM(Standard D2s v3 - Premium SSD) | forecast package of R | Auto ARIMA | Multivariate | Yes
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[ETS](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FETS) | 70.99 | 277 | 0.01 | Linux DSVM(Standard D2s v3 - Premium SSD) | forecast package of R | ETS | Multivariate | No
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[MeanForecast](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FMeanForecast) | 70.74 | 69.88 | 0.002 | Linux DSVM(Standard D2s v3 - Premium SSD) | forecast package of R | Mean forecast | Univariate | No
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[SeasonalNaive](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FSeasonalNaive) | 165.06 | 160.45 | 0.004 | Linux DSVM(Standard D2s v3 - Premium SSD) | forecast package of R | Seasonal Naive | Univariate | No
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[LightGBM](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FLightGBM) | 36.28 | 625.10 | 0.0167 | Linux DSVM (Standard D2s v3 - Premium SSD) | lightGBM package of Python | Gradient Boosting Decision Tree | Multivariate | Yes
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[DilatedCNN](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FDilatedCNN) | 37.09 | 413 | 0.1032 | Ubuntu VM(NC6 - Standard HDD) | Keras and Tensorflow | Python + Dilated convolutional neural network | Multivariate | Yes
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[RNN Encoder-Decoder](benchmarks%2FOrangeJuice_Pt_3Weeks_Weekly%2FRNN) | 37.68 | 669 | 0.2 | Ubuntu VM(NC6 - Standard HDD) | Tensorflow | Python + Encoder-decoder architecture of recurrent neural network | Multivariate | Yes
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The following chart compares the submissions performance on accuracy in %MAPE vs. Training and Scoring cost in $:
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## Build Status
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| Build Type | Branch | Status | | Branch | Status |
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| --- | --- | --- | --- | --- | --- |
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| **Python Linux CPU** | master | [](https://dev.azure.com/best-practices/forecasting/_build/latest?definitionId=12&branchName=master) | | staging | [](https://dev.azure.com/best-practices/forecasting/_build/latest?definitionId=12&branchName=chenhui/python_test_pipeline) |
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| **R Linux CPU** | master | [](https://dev.azure.com/best-practices/forecasting/_build/latest?definitionId=9&branchName=master) | | staging | [](https://dev.azure.com/best-practices/forecasting/_build/latest?definitionId=9&branchName=zhouf/r_test_pipeline) |
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# Forecasting Best Practices
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This repository contains examples and best practices for building Forecasting solutions and systems, provided as [Jupyter notebooks](examples) and [a library of utility functions](forecasting_lib). The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on forecasting problems.
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@ -1,59 +0,0 @@
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"""
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This script passes the input arguments of AzureML job to the R script train_validate_aml.R,
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and then passes the output of train_validate_aml.R back to AzureML.
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"""
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import subprocess
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import os
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import sys
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import getopt
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import pandas as pd
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from datetime import datetime
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from azureml.core import Run
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import time
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start_time = time.time()
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run = Run.get_submitted_run()
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base_command = "Rscript train_validate_aml.R"
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if __name__ == '__main__':
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opts, args = getopt.getopt(
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sys.argv[1:], '', ['path=', 'cv_path=', 'n_hidden_1=', 'n_hidden_2=',
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'iter_max=', 'penalty='])
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for opt, arg in opts:
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if opt == '--path':
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path = arg
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elif opt == '--cv_path':
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cv_path = arg
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elif opt == '--n_hidden_1':
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n_hidden_1 = arg
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elif opt == '--n_hidden_2':
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n_hidden_2 = arg
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elif opt == '--iter_max':
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iter_max = arg
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elif opt == '--penalty':
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penalty = arg
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time_stamp = datetime.now().strftime('%Y%m%d%H%M%S')
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task = " ".join([base_command,
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'--path', path,
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'--cv_path', cv_path,
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'--n_hidden_1', n_hidden_1,
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'--n_hidden_2', n_hidden_2,
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'--iter_max', iter_max,
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'--penalty', penalty,
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'--time_stamp', time_stamp])
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process = subprocess.call(task, shell=True)
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# process.communicate()
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# process.wait()
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output_file_name = 'cv_output_' + time_stamp + '.csv'
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result = pd.read_csv(os.path.join(cv_path, output_file_name))
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APL = result['loss'].mean()
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print(APL)
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print("--- %s seconds ---" % (time.time() - start_time))
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run.log('average pinball loss', APL)
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@ -1,77 +0,0 @@
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# coding: utf-8
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# Create input features for the Dilated Convolutional Neural Network (CNN) model.
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import os
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import sys
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import math
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import datetime
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import numpy as np
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import pandas as pd
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# Append TSPerf path to sys.path
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tsperf_dir = '.'
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if tsperf_dir not in sys.path:
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sys.path.append(tsperf_dir)
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# Import TSPerf components
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from utils import *
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import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
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def make_features(pred_round, train_dir, pred_steps, offset, store_list, brand_list):
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"""Create a dataframe of the input features.
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Args:
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pred_round (Integer): Prediction round
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train_dir (String): Path of the training data directory
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pred_steps (Integer): Number of prediction steps
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offset (Integer): Length of training data skipped in the retraining
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store_list (Numpy Array): List of all the store IDs
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brand_list (Numpy Array): List of all the brand IDs
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Returns:
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data_filled (Dataframe): Dataframe including the input features
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data_scaled (Dataframe): Dataframe including the normalized features
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"""
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# Load training data
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train_df = pd.read_csv(os.path.join(train_dir, 'train_round_'+str(pred_round+1)+'.csv'))
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train_df['move'] = train_df['logmove'].apply(lambda x: round(math.exp(x)))
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train_df = train_df[['store', 'brand', 'week', 'move']]
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# Create a dataframe to hold all necessary data
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week_list = range(bs.TRAIN_START_WEEK+offset, bs.TEST_END_WEEK_LIST[pred_round]+1)
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d = {'store': store_list,
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'brand': brand_list,
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'week': week_list}
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data_grid = df_from_cartesian_product(d)
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data_filled = pd.merge(data_grid, train_df, how='left',
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on=['store', 'brand', 'week'])
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# Get future price, deal, and advertisement info
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aux_df = pd.read_csv(os.path.join(train_dir, 'aux_round_'+str(pred_round+1)+'.csv'))
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data_filled = pd.merge(data_filled, aux_df, how='left',
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on=['store', 'brand', 'week'])
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# Create relative price feature
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price_cols = ['price1', 'price2', 'price3', 'price4', 'price5', 'price6', 'price7', 'price8', \
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'price9', 'price10', 'price11']
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data_filled['price'] = data_filled.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
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data_filled['avg_price'] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
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data_filled['price_ratio'] = data_filled['price'] / data_filled['avg_price']
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data_filled.drop(price_cols, axis=1, inplace=True)
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# Fill missing values
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data_filled = data_filled.groupby(['store', 'brand']). \
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apply(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
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|
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# Create datetime features
|
||||
data_filled['week_start'] = data_filled['week'].apply(lambda x: bs.FIRST_WEEK_START + datetime.timedelta(days=(x-1)*7))
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data_filled['month'] = data_filled['week_start'].apply(lambda x: x.month)
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data_filled['week_of_month'] = data_filled['week_start'].apply(lambda x: week_of_month(x))
|
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data_filled.drop('week_start', axis=1, inplace=True)
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|
||||
# Normalize the dataframe of features
|
||||
cols_normalize = data_filled.columns.difference(['store', 'brand', 'week'])
|
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data_scaled, min_max_scaler = normalize_dataframe(data_filled, cols_normalize)
|
||||
|
||||
return data_filled, data_scaled
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|
|
@ -1,199 +0,0 @@
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# coding: utf-8
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|
||||
# Train and score a Dilated Convolutional Neural Network (CNN) model using Keras package with TensorFlow backend.
|
||||
|
||||
import os
|
||||
import sys
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||||
import keras
|
||||
import random
|
||||
import argparse
|
||||
import numpy as np
|
||||
import pandas as pd
|
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import tensorflow as tf
|
||||
|
||||
from keras import optimizers
|
||||
from keras.layers import *
|
||||
from keras.models import Model, load_model
|
||||
from keras.callbacks import ModelCheckpoint
|
||||
|
||||
# Append TSPerf path to sys.path (assume we run the script from TSPerf directory)
|
||||
tsperf_dir = '.'
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
# Import TSPerf components
|
||||
from utils import *
|
||||
from make_features import make_features
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
# Model definition
|
||||
def create_dcnn_model(seq_len, kernel_size=2, n_filters=3, n_input_series=1, n_outputs=1):
|
||||
"""Create a Dilated CNN model.
|
||||
|
||||
Args:
|
||||
seq_len (Integer): Input sequence length
|
||||
kernel_size (Integer): Kernel size of each convolutional layer
|
||||
n_filters (Integer): Number of filters in each convolutional layer
|
||||
n_outputs (Integer): Number of outputs in the last layer
|
||||
|
||||
Returns:
|
||||
Keras Model object
|
||||
"""
|
||||
# Sequential input
|
||||
seq_in = Input(shape=(seq_len, n_input_series))
|
||||
|
||||
# Categorical input
|
||||
cat_fea_in = Input(shape=(2,), dtype='uint8')
|
||||
store_id = Lambda(lambda x: x[:, 0, None])(cat_fea_in)
|
||||
brand_id = Lambda(lambda x: x[:, 1, None])(cat_fea_in)
|
||||
store_embed = Embedding(MAX_STORE_ID+1, 7, input_length=1)(store_id)
|
||||
brand_embed = Embedding(MAX_BRAND_ID+1, 4, input_length=1)(brand_id)
|
||||
|
||||
# Dilated convolutional layers
|
||||
c1 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=1,
|
||||
padding='causal', activation='relu')(seq_in)
|
||||
c2 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=2,
|
||||
padding='causal', activation='relu')(c1)
|
||||
c3 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=4,
|
||||
padding='causal', activation='relu')(c2)
|
||||
|
||||
# Skip connections
|
||||
c4 = concatenate([c1, c3])
|
||||
|
||||
# Output of convolutional layers
|
||||
conv_out = Conv1D(8, 1, activation='relu')(c4)
|
||||
conv_out = Dropout(args.dropout_rate)(conv_out)
|
||||
conv_out = Flatten()(conv_out)
|
||||
|
||||
# Concatenate with categorical features
|
||||
x = concatenate([conv_out, Flatten()(store_embed), Flatten()(brand_embed)])
|
||||
x = Dense(16, activation='relu')(x)
|
||||
output = Dense(n_outputs, activation='linear')(x)
|
||||
|
||||
# Define model interface, loss function, and optimizer
|
||||
model = Model(inputs=[seq_in, cat_fea_in], outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--seed', type=int, dest='seed', default=1, help='random seed')
|
||||
parser.add_argument('--seq-len', type=int, dest='seq_len', default=15, help='length of the input sequence')
|
||||
parser.add_argument('--dropout-rate', type=float, dest='dropout_rate', default=0.01, help='dropout ratio')
|
||||
parser.add_argument('--batch-size', type=int, dest='batch_size', default=64, help='mini batch size for training')
|
||||
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.015, help='learning rate')
|
||||
parser.add_argument('--epochs', type=int, dest='epochs', default=25, help='# of epochs')
|
||||
args = parser.parse_args()
|
||||
|
||||
# Fix random seeds
|
||||
np.random.seed(args.seed)
|
||||
random.seed(args.seed)
|
||||
tf.set_random_seed(args.seed)
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = os.path.join(tsperf_dir, 'retail_sales', 'OrangeJuice_Pt_3Weeks_Weekly', 'data')
|
||||
SUBMISSION_DIR = os.path.join(tsperf_dir, 'retail_sales', 'OrangeJuice_Pt_3Weeks_Weekly', 'submissions', 'DilatedCNN')
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, 'train')
|
||||
|
||||
# Dataset parameters
|
||||
MAX_STORE_ID = 137
|
||||
MAX_BRAND_ID = 11
|
||||
|
||||
# Parameters of the model
|
||||
PRED_HORIZON = 3
|
||||
PRED_STEPS = 2
|
||||
SEQ_LEN = args.seq_len
|
||||
DYNAMIC_FEATURES = ['deal', 'feat', 'month', 'week_of_month', 'price', 'price_ratio']
|
||||
STATIC_FEATURES = ['store', 'brand']
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, 'train_round_1.csv'))
|
||||
store_list = train_df['store'].unique()
|
||||
brand_list = train_df['brand'].unique()
|
||||
store_brand = [(x,y) for x in store_list for y in brand_list]
|
||||
|
||||
# Train and predict for all forecast rounds
|
||||
pred_all = []
|
||||
file_name = os.path.join(SUBMISSION_DIR, 'dcnn_model.h5')
|
||||
for r in range(bs.NUM_ROUNDS):
|
||||
print('---- Round ' + str(r+1) + ' ----')
|
||||
offset = 0 if r==0 else 40+r*PRED_STEPS
|
||||
# Create features
|
||||
data_filled, data_scaled = make_features(r, TRAIN_DIR, PRED_STEPS, offset, store_list, brand_list)
|
||||
|
||||
# Create sequence array for 'move'
|
||||
start_timestep = 0
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r]-bs.TRAIN_START_WEEK-PRED_HORIZON
|
||||
train_input1 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, ['move'], start_timestep, end_timestep-offset)
|
||||
|
||||
# Create sequence array for other dynamic features
|
||||
start_timestep = PRED_HORIZON
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r]-bs.TRAIN_START_WEEK
|
||||
train_input2 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep, end_timestep-offset)
|
||||
|
||||
seq_in = np.concatenate([train_input1, train_input2], axis=2)
|
||||
|
||||
# Create array of static features
|
||||
total_timesteps = bs.TRAIN_END_WEEK_LIST[r]-bs.TRAIN_START_WEEK-SEQ_LEN-PRED_HORIZON+2
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps-offset, STATIC_FEATURES)
|
||||
|
||||
# Create training output
|
||||
start_timestep = SEQ_LEN+PRED_HORIZON-PRED_STEPS
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r]-bs.TRAIN_START_WEEK
|
||||
train_output = gen_sequence_array(data_filled, store_brand, PRED_STEPS, ['move'], start_timestep, end_timestep-offset)
|
||||
train_output = np.squeeze(train_output)
|
||||
|
||||
# Create and train model
|
||||
if r == 0:
|
||||
model = create_dcnn_model(seq_len=SEQ_LEN, n_filters=2, n_input_series=1+len(DYNAMIC_FEATURES), n_outputs=PRED_STEPS)
|
||||
adam = optimizers.Adam(lr=args.learning_rate)
|
||||
model.compile(loss='mape', optimizer=adam, metrics=['mape'])
|
||||
# Define checkpoint and fit model
|
||||
checkpoint = ModelCheckpoint(file_name, monitor='loss', save_best_only=True, mode='min', verbose=0)
|
||||
callbacks_list = [checkpoint]
|
||||
history = model.fit([seq_in, cat_fea_in], train_output, epochs=args.epochs, batch_size=args.batch_size, callbacks=callbacks_list, verbose=0)
|
||||
else:
|
||||
model = load_model(file_name)
|
||||
checkpoint = ModelCheckpoint(file_name, monitor='loss', save_best_only=True, mode='min', verbose=0)
|
||||
callbacks_list = [checkpoint]
|
||||
history = model.fit([seq_in, cat_fea_in], train_output, epochs=1, batch_size=args.batch_size, callbacks=callbacks_list, verbose=0)
|
||||
|
||||
# Get inputs for prediction
|
||||
start_timestep = bs.TEST_START_WEEK_LIST[r] - bs.TRAIN_START_WEEK - SEQ_LEN - PRED_HORIZON + PRED_STEPS
|
||||
end_timestep = bs.TEST_START_WEEK_LIST[r] - bs.TRAIN_START_WEEK + PRED_STEPS - 1 - PRED_HORIZON
|
||||
test_input1 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, ['move'], start_timestep-offset, end_timestep-offset)
|
||||
|
||||
start_timestep = bs.TEST_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK - SEQ_LEN + 1
|
||||
end_timestep = bs.TEST_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK
|
||||
test_input2 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep-offset, end_timestep-offset)
|
||||
|
||||
seq_in = np.concatenate([test_input1, test_input2], axis=2)
|
||||
|
||||
total_timesteps = 1
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps, STATIC_FEATURES)
|
||||
|
||||
# Make prediction
|
||||
pred = np.round(model.predict([seq_in, cat_fea_in]))
|
||||
|
||||
# Create dataframe for submission
|
||||
exp_output = data_filled[data_filled.week >= bs.TEST_START_WEEK_LIST[r]].reset_index(drop=True)
|
||||
exp_output = exp_output[['store', 'brand', 'week']]
|
||||
pred_df = exp_output.sort_values(['store', 'brand', 'week']).\
|
||||
loc[:,['store', 'brand', 'week']].\
|
||||
reset_index(drop=True)
|
||||
pred_df['weeks_ahead'] = pred_df['week'] - bs.TRAIN_END_WEEK_LIST[r]
|
||||
pred_df['round'] = r+1
|
||||
pred_df['prediction'] = np.reshape(pred, (pred.size, 1))
|
||||
pred_all.append(pred_df)
|
||||
|
||||
# Generate submission
|
||||
submission = pd.concat(pred_all, axis=0).reset_index(drop=True)
|
||||
submission = submission[['round', 'store', 'brand', 'week', 'weeks_ahead', 'prediction']]
|
||||
filename = 'submission_seed_' + str(args.seed) + '.csv'
|
||||
submission.to_csv(os.path.join(SUBMISSION_DIR, filename), index=False)
|
||||
print('Done')
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,200 +0,0 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Perform cross validation of a Dilated Convolutional Neural Network (CNN) model on the training data of the 1st forecast round.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import keras
|
||||
import argparse
|
||||
import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from utils import *
|
||||
from keras.layers import *
|
||||
from keras.models import Model
|
||||
from keras import optimizers
|
||||
from keras.utils import multi_gpu_model
|
||||
from azureml.core import Run
|
||||
|
||||
# Model definition
|
||||
def create_dcnn_model(seq_len, kernel_size=2, n_filters=3, n_input_series=1, n_outputs=1):
|
||||
"""Create a Dilated CNN model.
|
||||
|
||||
Args:
|
||||
seq_len (Integer): Input sequence length
|
||||
kernel_size (Integer): Kernel size of each convolutional layer
|
||||
n_filters (Integer): Number of filters in each convolutional layer
|
||||
n_outputs (Integer): Number of outputs in the last layer
|
||||
|
||||
Returns:
|
||||
Keras Model object
|
||||
"""
|
||||
# Sequential input
|
||||
seq_in = Input(shape=(seq_len, n_input_series))
|
||||
|
||||
# Categorical input
|
||||
cat_fea_in = Input(shape=(2,), dtype='uint8')
|
||||
store_id = Lambda(lambda x: x[:, 0, None])(cat_fea_in)
|
||||
brand_id = Lambda(lambda x: x[:, 1, None])(cat_fea_in)
|
||||
store_embed = Embedding(MAX_STORE_ID+1, 7, input_length=1)(store_id)
|
||||
brand_embed = Embedding(MAX_BRAND_ID+1, 4, input_length=1)(brand_id)
|
||||
|
||||
# Dilated convolutional layers
|
||||
c1 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=1,
|
||||
padding='causal', activation='relu')(seq_in)
|
||||
c2 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=2,
|
||||
padding='causal', activation='relu')(c1)
|
||||
c3 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=4,
|
||||
padding='causal', activation='relu')(c2)
|
||||
|
||||
# Skip connections
|
||||
c4 = concatenate([c1, c3])
|
||||
|
||||
# Output of convolutional layers
|
||||
conv_out = Conv1D(8, 1, activation='relu')(c4)
|
||||
conv_out = Dropout(args.dropout_rate)(conv_out)
|
||||
conv_out = Flatten()(conv_out)
|
||||
|
||||
# Concatenate with categorical features
|
||||
x = concatenate([conv_out, Flatten()(store_embed), Flatten()(brand_embed)])
|
||||
x = Dense(16, activation='relu')(x)
|
||||
output = Dense(n_outputs, activation='linear')(x)
|
||||
|
||||
# Define model interface, loss function, and optimizer
|
||||
model = Model(inputs=[seq_in, cat_fea_in], outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data-folder', type=str, dest='data_folder', help='data folder mounting point')
|
||||
parser.add_argument('--seq-len', type=int, dest='seq_len', default=20, help='length of the input sequence')
|
||||
parser.add_argument('--batch-size', type=int, dest='batch_size', default=64, help='mini batch size for training')
|
||||
parser.add_argument('--dropout-rate', type=float, dest='dropout_rate', default=0.10, help='dropout ratio')
|
||||
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.01, help='learning rate')
|
||||
parser.add_argument('--epochs', type=int, dest='epochs', default=30, help='# of epochs')
|
||||
args = parser.parse_args()
|
||||
args.dropout_rate = round(args.dropout_rate, 2)
|
||||
print(args)
|
||||
|
||||
# Start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = args.data_folder
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, 'train')
|
||||
|
||||
# Data and forecast problem parameters
|
||||
MAX_STORE_ID = 137
|
||||
MAX_BRAND_ID = 11
|
||||
PRED_HORIZON = 3
|
||||
PRED_STEPS = 2
|
||||
TRAIN_START_WEEK = 40
|
||||
TRAIN_END_WEEK_LIST = list(range(135,159,2))
|
||||
TEST_START_WEEK_LIST = list(range(137,161,2))
|
||||
TEST_END_WEEK_LIST = list(range(138,162,2))
|
||||
# The start datetime of the first week in the record
|
||||
FIRST_WEEK_START = pd.to_datetime('1989-09-14 00:00:00')
|
||||
|
||||
# Input sequence length and feature names
|
||||
SEQ_LEN = args.seq_len
|
||||
DYNAMIC_FEATURES = ['deal', 'feat', 'month', 'week_of_month', 'price', 'price_ratio']
|
||||
STATIC_FEATURES = ['store', 'brand']
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, 'train_round_1.csv'))
|
||||
store_list = train_df['store'].unique()
|
||||
brand_list = train_df['brand'].unique()
|
||||
store_brand = [(x,y) for x in store_list for y in brand_list]
|
||||
|
||||
# Train and validate the model using only the first round data
|
||||
r = 0
|
||||
print('---- Round ' + str(r+1) + ' ----')
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, 'train_round_'+str(r+1)+'.csv'))
|
||||
train_df['move'] = train_df['logmove'].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[['store', 'brand', 'week', 'move']]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
week_list = range(TRAIN_START_WEEK, TEST_END_WEEK_LIST[r]+1)
|
||||
d = {'store': store_list,
|
||||
'brand': brand_list,
|
||||
'week': week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(TRAIN_DIR, 'aux_round_'+str(r+1)+'.csv'))
|
||||
data_filled = pd.merge(data_filled, aux_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = ['price1', 'price2', 'price3', 'price4', 'price5', 'price6', 'price7', 'price8', \
|
||||
'price9', 'price10', 'price11']
|
||||
data_filled['price'] = data_filled.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
|
||||
data_filled['avg_price'] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled['price_ratio'] = data_filled.apply(lambda x: x['price'] / x['avg_price'], axis=1)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(['store', 'brand']). \
|
||||
apply(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
|
||||
|
||||
# Create datetime features
|
||||
data_filled['week_start'] = data_filled['week'].apply(lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x-1)*7))
|
||||
data_filled['day'] = data_filled['week_start'].apply(lambda x: x.day)
|
||||
data_filled['week_of_month'] = data_filled['week_start'].apply(lambda x: week_of_month(x))
|
||||
data_filled['month'] = data_filled['week_start'].apply(lambda x: x.month)
|
||||
data_filled.drop('week_start', axis=1, inplace=True)
|
||||
|
||||
# Normalize the dataframe of features
|
||||
cols_normalize = data_filled.columns.difference(['store', 'brand', 'week'])
|
||||
data_scaled, min_max_scaler = normalize_dataframe(data_filled, cols_normalize)
|
||||
|
||||
# Create sequence array for 'move'
|
||||
start_timestep = 0
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r]-TRAIN_START_WEEK-PRED_HORIZON
|
||||
train_input1 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, ['move'], start_timestep, end_timestep)
|
||||
|
||||
# Create sequence array for other dynamic features
|
||||
start_timestep = PRED_HORIZON
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r]-TRAIN_START_WEEK
|
||||
train_input2 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep, end_timestep)
|
||||
|
||||
seq_in = np.concatenate((train_input1, train_input2), axis=2)
|
||||
|
||||
# Create array of static features
|
||||
total_timesteps = TRAIN_END_WEEK_LIST[r]-TRAIN_START_WEEK-SEQ_LEN-PRED_HORIZON+2
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps, STATIC_FEATURES)
|
||||
|
||||
# Create training output
|
||||
start_timestep = SEQ_LEN+PRED_HORIZON-PRED_STEPS
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r]-TRAIN_START_WEEK
|
||||
train_output = gen_sequence_array(data_filled, store_brand, PRED_STEPS, ['move'], start_timestep, end_timestep)
|
||||
train_output = np.squeeze(train_output)
|
||||
|
||||
# Create model
|
||||
model = create_dcnn_model(seq_len=SEQ_LEN, n_filters=2, n_input_series=1+len(DYNAMIC_FEATURES), n_outputs=PRED_STEPS)
|
||||
|
||||
# Convert to GPU model
|
||||
try:
|
||||
model = multi_gpu_model(model)
|
||||
print('Training using multiple GPUs...')
|
||||
except:
|
||||
print('Training using single GPU or CPU...')
|
||||
|
||||
adam = optimizers.Adam(lr=args.learning_rate)
|
||||
model.compile(loss='mape', optimizer=adam, metrics=['mape', 'mae'])
|
||||
|
||||
# Model training and validation
|
||||
history = model.fit([seq_in, cat_fea_in], train_output, epochs=args.epochs, batch_size=args.batch_size, validation_split=0.05)
|
||||
val_loss = history.history['val_loss'][-1]
|
||||
print('Validation loss is {}'.format(val_loss))
|
||||
|
||||
# Log the validation loss/MAPE
|
||||
run.log('MAPE', np.float(val_loss))
|
||||
|
||||
|
||||
|
|
@ -1,128 +0,0 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Train and score a boosted decision tree model using [LightGBM Python package](https://github.com/Microsoft/LightGBM) from Microsoft,
|
||||
# which is a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
|
||||
import warnings
|
||||
warnings.filterwarnings('ignore')
|
||||
|
||||
# Append TSPerf path to sys.path
|
||||
tsperf_dir = os.getcwd()
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
from make_features import make_features
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
def make_predictions(df, model):
|
||||
"""Predict sales with the trained GBM model.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Dataframe including all needed features
|
||||
model (Model): Trained GBM model
|
||||
|
||||
Returns:
|
||||
Dataframe including the predicted sales of every store-brand
|
||||
"""
|
||||
predictions = pd.DataFrame({'move': model.predict(df.drop('move', axis=1))})
|
||||
predictions['move'] = predictions['move'].apply(lambda x: round(x))
|
||||
return pd.concat([df[['brand', 'store', 'week']].reset_index(drop=True), predictions], axis=1)
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--seed', type=int, dest='seed', default=1, help='Random seed of GBM model')
|
||||
parser.add_argument('--num-leaves', type=int, dest='num_leaves', default=124, help='# of leaves of the tree')
|
||||
parser.add_argument('--min-data-in-leaf', type=int, dest='min_data_in_leaf', default=340, help='minimum # of samples in each leaf')
|
||||
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.1, help='learning rate')
|
||||
parser.add_argument('--feature-fraction', type=float, dest='feature_fraction', default=0.65, help='ratio of features used in each iteration')
|
||||
parser.add_argument('--bagging-fraction', type=float, dest='bagging_fraction', default=0.87, help='ratio of samples used in each iteration')
|
||||
parser.add_argument('--bagging-freq', type=int, dest='bagging_freq', default=19, help='bagging frequency')
|
||||
parser.add_argument('--max-rounds', type=int, dest='max_rounds', default=940, help='# of boosting iterations')
|
||||
parser.add_argument('--max-lag', type=int, dest='max_lag', default=19, help='max lag of unit sales')
|
||||
parser.add_argument('--window-size', type=int, dest='window_size', default=40, help='window size of moving average of unit sales')
|
||||
args = parser.parse_args()
|
||||
print(args)
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = os.path.join(tsperf_dir, 'retail_sales', 'OrangeJuice_Pt_3Weeks_Weekly', 'data')
|
||||
SUBMISSION_DIR = os.path.join(tsperf_dir, 'retail_sales', 'OrangeJuice_Pt_3Weeks_Weekly', 'submissions', 'LightGBM')
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, 'train')
|
||||
|
||||
# Parameters of GBM model
|
||||
params = {
|
||||
'objective': 'mape',
|
||||
'num_leaves': args.num_leaves,
|
||||
'min_data_in_leaf': args.min_data_in_leaf,
|
||||
'learning_rate': args.learning_rate,
|
||||
'feature_fraction': args.feature_fraction,
|
||||
'bagging_fraction': args.bagging_fraction,
|
||||
'bagging_freq': args.bagging_freq,
|
||||
'num_rounds': args.max_rounds,
|
||||
'early_stopping_rounds': 125,
|
||||
'num_threads': 4,
|
||||
'seed': args.seed
|
||||
}
|
||||
|
||||
# Lags and categorical features
|
||||
lags = np.arange(2, args.max_lag+1)
|
||||
used_columns = ['store', 'brand', 'week', 'week_of_month', 'month', 'deal', 'feat', 'move', 'price', 'price_ratio']
|
||||
categ_fea = ['store', 'brand', 'deal']
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, 'train_round_1.csv'))
|
||||
store_list = train_df['store'].unique()
|
||||
brand_list = train_df['brand'].unique()
|
||||
|
||||
# Train and predict for all forecast rounds
|
||||
pred_all = []
|
||||
metric_all = []
|
||||
for r in range(bs.NUM_ROUNDS):
|
||||
print('---- Round ' + str(r+1) + ' ----')
|
||||
# Create features
|
||||
features = make_features(r, TRAIN_DIR, lags, args.window_size, 0, used_columns, store_list, brand_list)
|
||||
train_fea = features[features.week <= bs.TRAIN_END_WEEK_LIST[r]].reset_index(drop=True)
|
||||
|
||||
# Drop rows with NaN values
|
||||
train_fea.dropna(inplace=True)
|
||||
|
||||
# Create training set
|
||||
dtrain = lgb.Dataset(train_fea.drop('move', axis=1, inplace=False),
|
||||
label = train_fea['move'])
|
||||
if r %3 == 0:
|
||||
# Train GBM model
|
||||
print('Training model...')
|
||||
bst = lgb.train(
|
||||
params,
|
||||
dtrain,
|
||||
valid_sets = [dtrain],
|
||||
categorical_feature = categ_fea,
|
||||
verbose_eval = False
|
||||
)
|
||||
|
||||
# Generate forecasts
|
||||
print('Making predictions...')
|
||||
test_fea = features[features.week >= bs.TEST_START_WEEK_LIST[r]].reset_index(drop=True)
|
||||
pred = make_predictions(test_fea, bst).sort_values(by=['store','brand', 'week']).reset_index(drop=True)
|
||||
# Additional columns required by the submission format
|
||||
pred['round'] = r+1
|
||||
pred['weeks_ahead'] = pred['week'] - bs.TRAIN_END_WEEK_LIST[r]
|
||||
# Keep the predictions
|
||||
pred_all.append(pred)
|
||||
|
||||
# Generate submission
|
||||
submission = pd.concat(pred_all, axis=0)
|
||||
submission.rename(columns={'move': 'prediction'}, inplace=True)
|
||||
submission = submission[['round', 'store', 'brand', 'week', 'weeks_ahead', 'prediction']]
|
||||
filename = 'submission_seed_' + str(args.seed) + '.csv'
|
||||
submission.to_csv(os.path.join(SUBMISSION_DIR, filename), index=False)
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,219 +0,0 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Perform cross validation of a boosted decision tree model on the training data of the 1st forecast round.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import argparse
|
||||
import datetime
|
||||
import itertools
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
from azureml.core import Run
|
||||
from sklearn.model_selection import train_test_split
|
||||
from utils import week_of_month, df_from_cartesian_product
|
||||
|
||||
def lagged_features(df, lags):
|
||||
"""Create lagged features based on time series data.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Input time series data sorted by time
|
||||
lags (List): Lag lengths
|
||||
|
||||
Returns:
|
||||
fea (Dataframe): Lagged features
|
||||
"""
|
||||
df_list = []
|
||||
for lag in lags:
|
||||
df_shifted = df.shift(lag)
|
||||
df_shifted.columns = [x + '_lag' + str(lag) for x in df_shifted.columns]
|
||||
df_list.append(df_shifted)
|
||||
fea = pd.concat(df_list, axis=1)
|
||||
return fea
|
||||
|
||||
def moving_averages(df, start_step, window_size=None):
|
||||
"""Compute averages of every feature over moving time windows.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Input features as a dataframe
|
||||
start_step (Integer): Starting time step of rolling mean
|
||||
window_size (Integer): Windows size of rolling mean
|
||||
|
||||
Returns:
|
||||
fea (Dataframe): Dataframe consisting of the moving averages
|
||||
"""
|
||||
if window_size == None: # Use a large window to compute average over all historical data
|
||||
window_size = df.shape[0]
|
||||
fea = df.shift(start_step).rolling(min_periods=1, center=False, window=window_size).mean()
|
||||
fea.columns = fea.columns + '_mean'
|
||||
return fea
|
||||
|
||||
def combine_features(df, lag_fea, lags, window_size, used_columns):
|
||||
"""Combine different features for a certain store-brand.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Time series data of a certain store-brand
|
||||
lag_fea (List): A list of column names for creating lagged features
|
||||
lags (Numpy Array): Numpy array including all the lags
|
||||
window_size (Integer): Windows size of rolling mean
|
||||
used_columns (List): A list of names of columns used in model training (including target variable)
|
||||
|
||||
Returns:
|
||||
fea_all (Dataframe): Dataframe including all features for the specific store-brand
|
||||
"""
|
||||
lagged_fea = lagged_features(df[lag_fea], lags)
|
||||
moving_avg = moving_averages(df[lag_fea], 2, window_size)
|
||||
fea_all = pd.concat([df[used_columns], lagged_fea, moving_avg], axis=1)
|
||||
return fea_all
|
||||
|
||||
def make_predictions(df, model):
|
||||
"""Predict sales with the trained GBM model.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Dataframe including all needed features
|
||||
model (Model): Trained GBM model
|
||||
|
||||
Returns:
|
||||
Dataframe including the predicted sales of a certain store-brand
|
||||
"""
|
||||
predictions = pd.DataFrame({'move': model.predict(df.drop('move', axis=1))})
|
||||
predictions['move'] = predictions['move'].apply(lambda x: round(x))
|
||||
return pd.concat([df[['brand', 'store', 'week']].reset_index(drop=True), predictions], axis=1)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data-folder', type=str, dest='data_folder', default='.', help='data folder mounting point')
|
||||
parser.add_argument('--num-leaves', type=int, dest='num_leaves', default=64, help='# of leaves of the tree')
|
||||
parser.add_argument('--min-data-in-leaf', type=int, dest='min_data_in_leaf', default=50, help='minimum # of samples in each leaf')
|
||||
parser.add_argument('--learning-rate', type=float, dest='learning_rate', default=0.001, help='learning rate')
|
||||
parser.add_argument('--feature-fraction', type=float, dest='feature_fraction', default=1.0, help='ratio of features used in each iteration')
|
||||
parser.add_argument('--bagging-fraction', type=float, dest='bagging_fraction', default=1.0, help='ratio of samples used in each iteration')
|
||||
parser.add_argument('--bagging-freq', type=int, dest='bagging_freq', default=1, help='bagging frequency')
|
||||
parser.add_argument('--max-rounds', type=int, dest='max_rounds', default=400, help='# of boosting iterations')
|
||||
parser.add_argument('--max-lag', type=int, dest='max_lag', default=10, help='max lag of unit sales')
|
||||
parser.add_argument('--window-size', type=int, dest='window_size', default=10, help='window size of moving average of unit sales')
|
||||
args = parser.parse_args()
|
||||
args.feature_fraction = round(args.feature_fraction, 2)
|
||||
args.bagging_fraction = round(args.bagging_fraction, 2)
|
||||
print(args)
|
||||
|
||||
# Start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = args.data_folder
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, 'train')
|
||||
|
||||
# Data and forecast problem parameters
|
||||
TRAIN_START_WEEK = 40
|
||||
TRAIN_END_WEEK_LIST = list(range(135,159,2))
|
||||
TEST_START_WEEK_LIST = list(range(137,161,2))
|
||||
TEST_END_WEEK_LIST = list(range(138,162,2))
|
||||
# The start datetime of the first week in the record
|
||||
FIRST_WEEK_START = pd.to_datetime('1989-09-14 00:00:00')
|
||||
|
||||
# Parameters of GBM model
|
||||
params = {
|
||||
'objective': 'mape',
|
||||
'num_leaves': args.num_leaves,
|
||||
'min_data_in_leaf': args.min_data_in_leaf,
|
||||
'learning_rate': args.learning_rate,
|
||||
'feature_fraction': args.feature_fraction,
|
||||
'bagging_fraction': args.bagging_fraction,
|
||||
'bagging_freq': args.bagging_freq,
|
||||
'num_rounds': args.max_rounds,
|
||||
'early_stopping_rounds': 125,
|
||||
'num_threads': 16
|
||||
}
|
||||
|
||||
# Lags and used column names
|
||||
lags = np.arange(2, args.max_lag+1)
|
||||
used_columns = ['store', 'brand', 'week', 'week_of_month', 'month', 'deal', 'feat', 'move', 'price', 'price_ratio']
|
||||
categ_fea = ['store', 'brand', 'deal']
|
||||
|
||||
# Train and validate the model using only the first round data
|
||||
r = 0
|
||||
print('---- Round ' + str(r+1) + ' ----')
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, 'train_round_'+str(r+1)+'.csv'))
|
||||
train_df['move'] = train_df['logmove'].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[['store', 'brand', 'week', 'move']]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
store_list = train_df['store'].unique()
|
||||
brand_list = train_df['brand'].unique()
|
||||
week_list = range(TRAIN_START_WEEK, TEST_END_WEEK_LIST[r]+1)
|
||||
d = {'store': store_list,
|
||||
'brand': brand_list,
|
||||
'week': week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(TRAIN_DIR, 'aux_round_'+str(r+1)+'.csv'))
|
||||
data_filled = pd.merge(data_filled, aux_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = ['price1', 'price2', 'price3', 'price4', 'price5', 'price6', 'price7', 'price8', \
|
||||
'price9', 'price10', 'price11']
|
||||
data_filled['price'] = data_filled.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
|
||||
data_filled['avg_price'] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled['price_ratio'] = data_filled['price'] / data_filled['avg_price']
|
||||
data_filled.drop(price_cols, axis=1, inplace=True)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(['store', 'brand']).apply(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
|
||||
|
||||
# Create datetime features
|
||||
data_filled['week_start'] = data_filled['week'].apply(lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x-1)*7))
|
||||
data_filled['year'] = data_filled['week_start'].apply(lambda x: x.year)
|
||||
data_filled['month'] = data_filled['week_start'].apply(lambda x: x.month)
|
||||
data_filled['week_of_month'] = data_filled['week_start'].apply(lambda x: week_of_month(x))
|
||||
data_filled['day'] = data_filled['week_start'].apply(lambda x: x.day)
|
||||
data_filled.drop('week_start', axis=1, inplace=True)
|
||||
|
||||
# Create other features (lagged features, moving averages, etc.)
|
||||
features = data_filled.groupby(['store','brand']).apply(lambda x: combine_features(x, ['move'], lags, args.window_size, used_columns))
|
||||
train_fea = features[features.week <= TRAIN_END_WEEK_LIST[r]].reset_index(drop=True)
|
||||
|
||||
# Drop rows with NaN values
|
||||
train_fea.dropna(inplace=True)
|
||||
|
||||
# Model training and validation
|
||||
# Create a training/validation split
|
||||
train_fea, valid_fea, train_label, valid_label = train_test_split(train_fea.drop('move', axis=1, inplace=False), \
|
||||
train_fea['move'], test_size=0.05, random_state=1)
|
||||
dtrain = lgb.Dataset(train_fea, train_label)
|
||||
dvalid = lgb.Dataset(valid_fea, valid_label)
|
||||
# A dictionary to record training results
|
||||
evals_result = {}
|
||||
# Train GBM model
|
||||
bst = lgb.train(
|
||||
params,
|
||||
dtrain,
|
||||
valid_sets = [dtrain, dvalid],
|
||||
categorical_feature = categ_fea,
|
||||
evals_result = evals_result
|
||||
)
|
||||
# Get final training loss & validation loss
|
||||
train_loss = evals_result['training']['mape'][-1]
|
||||
valid_loss = evals_result['valid_1']['mape'][-1]
|
||||
print('Final training loss is {}'.format(train_loss))
|
||||
print('Final validation loss is {}'.format(valid_loss))
|
||||
|
||||
# Log the validation loss/MAPE
|
||||
run.log('MAPE', np.float(valid_loss)*100)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,5 +1,7 @@
|
|||
### Benchmarks
|
||||
|
||||
TODO: Modify below based on the benchmarks we include.
|
||||
|
||||
Each entry below describes a forecasting benchmark.
|
||||
|
||||
| **Benchmark** | **Description** |
|
||||
|
|
|
|||
|
|
@ -9,11 +9,10 @@ import getopt
|
|||
|
||||
import localpath
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering \
|
||||
import compute_features
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering import compute_features
|
||||
|
||||
SUBMISSIONS_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR,'data')
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR, "data")
|
||||
print("Data directory used: {}".format(DATA_DIR))
|
||||
|
||||
OUTPUT_DIR = os.path.join(DATA_DIR, "features")
|
||||
|
|
@ -40,14 +39,8 @@ feature_config_list = [
|
|||
("temporal", {"feature_list": ["hour_of_day", "month_of_year"]}),
|
||||
("annual_fourier", {"n_harmonics": 3}),
|
||||
("weekly_fourier", {"n_harmonics": 3}),
|
||||
(
|
||||
"previous_year_load_lag",
|
||||
{"input_col_names": "DEMAND", "round_agg_result": True},
|
||||
),
|
||||
(
|
||||
"previous_year_temp_lag",
|
||||
{"input_col_names": "DryBulb", "round_agg_result": True},
|
||||
),
|
||||
("previous_year_load_lag", {"input_col_names": "DEMAND", "round_agg_result": True},),
|
||||
("previous_year_temp_lag", {"input_col_names": "DryBulb", "round_agg_result": True},),
|
||||
]
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
@ -55,9 +48,7 @@ if __name__ == "__main__":
|
|||
for opt, arg in opts:
|
||||
if opt == "--submission":
|
||||
submission_folder = arg
|
||||
output_data_dir = os.path.join(
|
||||
SUBMISSIONS_DIR, submission_folder, "data"
|
||||
)
|
||||
output_data_dir = os.path.join(SUBMISSIONS_DIR, submission_folder, "data")
|
||||
if not os.path.isdir(output_data_dir):
|
||||
os.mkdir(output_data_dir)
|
||||
OUTPUT_DIR = os.path.join(output_data_dir, "features")
|
||||
|
|
@ -3,6 +3,7 @@ This script inserts the TSPerf directory into sys.path, so that scripts can impo
|
|||
"""
|
||||
|
||||
import os, sys
|
||||
|
||||
_CURR_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
TSPERF_DIR = os.path.dirname(os.path.dirname(os.path.dirname(_CURR_DIR)))
|
||||
|
||||
|
|
@ -9,11 +9,10 @@ import getopt
|
|||
|
||||
import localpath
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering \
|
||||
import compute_features
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering import compute_features
|
||||
|
||||
SUBMISSIONS_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR,'data')
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR, "data")
|
||||
print("Data directory used: {}".format(DATA_DIR))
|
||||
|
||||
OUTPUT_DIR = os.path.join(DATA_DIR, "features")
|
||||
|
|
@ -40,14 +39,8 @@ feature_config_list = [
|
|||
("temporal", {"feature_list": ["hour_of_day", "month_of_year"]}),
|
||||
("annual_fourier", {"n_harmonics": 3}),
|
||||
("weekly_fourier", {"n_harmonics": 3}),
|
||||
(
|
||||
"previous_year_load_lag",
|
||||
{"input_col_names": "DEMAND", "round_agg_result": True},
|
||||
),
|
||||
(
|
||||
"previous_year_temp_lag",
|
||||
{"input_col_names": "DryBulb", "round_agg_result": True},
|
||||
),
|
||||
("previous_year_load_lag", {"input_col_names": "DEMAND", "round_agg_result": True},),
|
||||
("previous_year_temp_lag", {"input_col_names": "DryBulb", "round_agg_result": True},),
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -56,9 +49,7 @@ if __name__ == "__main__":
|
|||
for opt, arg in opts:
|
||||
if opt == "--submission":
|
||||
submission_folder = arg
|
||||
output_data_dir = os.path.join(
|
||||
SUBMISSIONS_DIR, submission_folder, "data"
|
||||
)
|
||||
output_data_dir = os.path.join(SUBMISSIONS_DIR, submission_folder, "data")
|
||||
if not os.path.isdir(output_data_dir):
|
||||
os.mkdir(output_data_dir)
|
||||
OUTPUT_DIR = os.path.join(output_data_dir, "features")
|
||||
|
|
@ -5,6 +5,7 @@ localpath.py file.
|
|||
"""
|
||||
|
||||
import os, sys
|
||||
|
||||
_CURR_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
TSPERF_DIR = os.path.dirname(os.path.dirname(os.path.dirname(_CURR_DIR)))
|
||||
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
"""
|
||||
This script passes the input arguments of AzureML job to the R script train_validate_aml.R,
|
||||
and then passes the output of train_validate_aml.R back to AzureML.
|
||||
"""
|
||||
|
||||
import subprocess
|
||||
import os
|
||||
import sys
|
||||
import getopt
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from azureml.core import Run
|
||||
import time
|
||||
|
||||
start_time = time.time()
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
base_command = "Rscript train_validate_aml.R"
|
||||
|
||||
if __name__ == "__main__":
|
||||
opts, args = getopt.getopt(
|
||||
sys.argv[1:], "", ["path=", "cv_path=", "n_hidden_1=", "n_hidden_2=", "iter_max=", "penalty="]
|
||||
)
|
||||
for opt, arg in opts:
|
||||
if opt == "--path":
|
||||
path = arg
|
||||
elif opt == "--cv_path":
|
||||
cv_path = arg
|
||||
elif opt == "--n_hidden_1":
|
||||
n_hidden_1 = arg
|
||||
elif opt == "--n_hidden_2":
|
||||
n_hidden_2 = arg
|
||||
elif opt == "--iter_max":
|
||||
iter_max = arg
|
||||
elif opt == "--penalty":
|
||||
penalty = arg
|
||||
time_stamp = datetime.now().strftime("%Y%m%d%H%M%S")
|
||||
task = " ".join(
|
||||
[
|
||||
base_command,
|
||||
"--path",
|
||||
path,
|
||||
"--cv_path",
|
||||
cv_path,
|
||||
"--n_hidden_1",
|
||||
n_hidden_1,
|
||||
"--n_hidden_2",
|
||||
n_hidden_2,
|
||||
"--iter_max",
|
||||
iter_max,
|
||||
"--penalty",
|
||||
penalty,
|
||||
"--time_stamp",
|
||||
time_stamp,
|
||||
]
|
||||
)
|
||||
process = subprocess.call(task, shell=True)
|
||||
|
||||
# process.communicate()
|
||||
# process.wait()
|
||||
|
||||
output_file_name = "cv_output_" + time_stamp + ".csv"
|
||||
result = pd.read_csv(os.path.join(cv_path, output_file_name))
|
||||
|
||||
APL = result["loss"].mean()
|
||||
|
||||
print(APL)
|
||||
print("--- %s seconds ---" % (time.time() - start_time))
|
||||
|
||||
run.log("average pinball loss", APL)
|
||||
|
|
@ -10,11 +10,10 @@ import getopt
|
|||
|
||||
import localpath
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering \
|
||||
import compute_features
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering import compute_features
|
||||
|
||||
SUBMISSIONS_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR,'data')
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR, "data")
|
||||
print("Data directory used: {}".format(DATA_DIR))
|
||||
|
||||
OUTPUT_DIR = os.path.join(DATA_DIR, "features")
|
||||
|
|
@ -41,14 +40,8 @@ feature_config_list = [
|
|||
("temporal", {"feature_list": ["hour_of_day", "month_of_year"]}),
|
||||
("annual_fourier", {"n_harmonics": 3}),
|
||||
("weekly_fourier", {"n_harmonics": 3}),
|
||||
(
|
||||
"previous_year_load_lag",
|
||||
{"input_col_names": "DEMAND", "round_agg_result": True},
|
||||
),
|
||||
(
|
||||
"previous_year_temp_lag",
|
||||
{"input_col_names": "DryBulb", "round_agg_result": True},
|
||||
),
|
||||
("previous_year_load_lag", {"input_col_names": "DEMAND", "round_agg_result": True},),
|
||||
("previous_year_temp_lag", {"input_col_names": "DryBulb", "round_agg_result": True},),
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -57,9 +50,7 @@ if __name__ == "__main__":
|
|||
for opt, arg in opts:
|
||||
if opt == "--submission":
|
||||
submission_folder = arg
|
||||
output_data_dir = os.path.join(
|
||||
SUBMISSIONS_DIR, submission_folder, "data"
|
||||
)
|
||||
output_data_dir = os.path.join(SUBMISSIONS_DIR, submission_folder, "data")
|
||||
if not os.path.isdir(output_data_dir):
|
||||
os.mkdir(output_data_dir)
|
||||
OUTPUT_DIR = os.path.join(output_data_dir, "features")
|
||||
|
|
@ -4,6 +4,7 @@ all the modules in TSPerf. Each submission folder needs its own localpath.py fil
|
|||
"""
|
||||
|
||||
import os, sys
|
||||
|
||||
_CURR_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
TSPERF_DIR = os.path.dirname(os.path.dirname(os.path.dirname(_CURR_DIR)))
|
||||
|
||||
|
|
@ -9,11 +9,10 @@ import getopt
|
|||
|
||||
import localpath
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering \
|
||||
import compute_features
|
||||
|
||||
from tsperf.benchmarking.GEFCom2017_D_Prob_MT_hourly.feature_engineering import compute_features
|
||||
|
||||
SUBMISSIONS_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR,'data')
|
||||
DATA_DIR = os.path.join(SUBMISSIONS_DIR, "data")
|
||||
print("Data directory used: {}".format(DATA_DIR))
|
||||
|
||||
OUTPUT_DIR = os.path.join(DATA_DIR, "features")
|
||||
|
|
@ -59,23 +58,11 @@ feature_config_list = [
|
|||
("normalized_datehour", {}),
|
||||
("normalized_year", {}),
|
||||
("day_type", {"holiday_col_name": HOLIDAY_COLNAME}),
|
||||
(
|
||||
"previous_year_load_lag",
|
||||
{"input_col_names": "DEMAND", "round_agg_result": True},
|
||||
),
|
||||
(
|
||||
"previous_year_temp_lag",
|
||||
{"input_col_names": ["DryBulb", "DewPnt"], "round_agg_result": True},
|
||||
),
|
||||
("previous_year_load_lag", {"input_col_names": "DEMAND", "round_agg_result": True},),
|
||||
("previous_year_temp_lag", {"input_col_names": ["DryBulb", "DewPnt"], "round_agg_result": True},),
|
||||
(
|
||||
"recent_load_lag",
|
||||
{
|
||||
"input_col_names": "DEMAND",
|
||||
"start_week": 10,
|
||||
"window_size": 4,
|
||||
"agg_count": 8,
|
||||
"round_agg_result": True,
|
||||
},
|
||||
{"input_col_names": "DEMAND", "start_week": 10, "window_size": 4, "agg_count": 8, "round_agg_result": True,},
|
||||
),
|
||||
(
|
||||
"recent_temp_lag",
|
||||
|
|
@ -95,9 +82,7 @@ if __name__ == "__main__":
|
|||
for opt, arg in opts:
|
||||
if opt == "--submission":
|
||||
submission_folder = arg
|
||||
output_data_dir = os.path.join(
|
||||
SUBMISSIONS_DIR, submission_folder, "data"
|
||||
)
|
||||
output_data_dir = os.path.join(SUBMISSIONS_DIR, submission_folder, "data")
|
||||
if not os.path.isdir(output_data_dir):
|
||||
os.mkdir(output_data_dir)
|
||||
OUTPUT_DIR = os.path.join(output_data_dir, "features")
|
||||
|
|
@ -105,10 +90,5 @@ if __name__ == "__main__":
|
|||
os.mkdir(OUTPUT_DIR)
|
||||
|
||||
compute_features(
|
||||
TRAIN_DATA_DIR,
|
||||
TEST_DATA_DIR,
|
||||
OUTPUT_DIR,
|
||||
DF_CONFIG,
|
||||
feature_config_list,
|
||||
filter_by_month=False,
|
||||
TRAIN_DATA_DIR, TEST_DATA_DIR, OUTPUT_DIR, DF_CONFIG, feature_config_list, filter_by_month=False,
|
||||
)
|
||||
|
|
@ -13,6 +13,7 @@ from skgarden.quantile.tree import DecisionTreeQuantileRegressor
|
|||
from skgarden.quantile.ensemble import generate_sample_indices
|
||||
from ensemble_parallel_utils import weighted_percentile_vectorized
|
||||
|
||||
|
||||
class BaseForestQuantileRegressor(ForestRegressor):
|
||||
"""Training and scoring of Quantile Regression Random Forest
|
||||
|
||||
|
|
@ -34,6 +35,7 @@ class BaseForestQuantileRegressor(ForestRegressor):
|
|||
a weight of zero when estimator j is fit, then the value is -1.
|
||||
|
||||
"""
|
||||
|
||||
def fit(self, X, y):
|
||||
"""Builds a forest from the training set (X, y).
|
||||
|
||||
|
|
@ -68,8 +70,7 @@ class BaseForestQuantileRegressor(ForestRegressor):
|
|||
Returns self.
|
||||
"""
|
||||
# apply method requires X to be of dtype np.float32
|
||||
X, y = check_X_y(
|
||||
X, y, accept_sparse="csc", dtype=np.float32, multi_output=False)
|
||||
X, y = check_X_y(X, y, accept_sparse="csc", dtype=np.float32, multi_output=False)
|
||||
super(BaseForestQuantileRegressor, self).fit(X, y)
|
||||
|
||||
self.y_train_ = y
|
||||
|
|
@ -78,8 +79,7 @@ class BaseForestQuantileRegressor(ForestRegressor):
|
|||
|
||||
for i, est in enumerate(self.estimators_):
|
||||
if self.bootstrap:
|
||||
bootstrap_indices = generate_sample_indices(
|
||||
est.random_state, len(y))
|
||||
bootstrap_indices = generate_sample_indices(est.random_state, len(y))
|
||||
else:
|
||||
bootstrap_indices = np.arange(len(y))
|
||||
|
||||
|
|
@ -87,8 +87,7 @@ class BaseForestQuantileRegressor(ForestRegressor):
|
|||
y_train_leaves = est.y_train_leaves_
|
||||
for curr_leaf in np.unique(y_train_leaves):
|
||||
y_ind = y_train_leaves == curr_leaf
|
||||
self.y_weights_[i, y_ind] = (
|
||||
est_weights[y_ind] / np.sum(est_weights[y_ind]))
|
||||
self.y_weights_[i, y_ind] = est_weights[y_ind] / np.sum(est_weights[y_ind])
|
||||
|
||||
self.y_train_leaves_[i, bootstrap_indices] = y_train_leaves[bootstrap_indices]
|
||||
|
||||
|
|
@ -167,21 +166,24 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor):
|
|||
oob_prediction_ : array of shape = [n_samples]
|
||||
Prediction computed with out-of-bag estimate on the training set.
|
||||
"""
|
||||
def __init__(self,
|
||||
n_estimators=10,
|
||||
criterion='mse',
|
||||
max_depth=None,
|
||||
min_samples_split=2,
|
||||
min_samples_leaf=1,
|
||||
min_weight_fraction_leaf=0.0,
|
||||
max_features='auto',
|
||||
max_leaf_nodes=None,
|
||||
bootstrap=True,
|
||||
oob_score=False,
|
||||
n_jobs=1,
|
||||
random_state=None,
|
||||
verbose=0,
|
||||
warm_start=False):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_estimators=10,
|
||||
criterion="mse",
|
||||
max_depth=None,
|
||||
min_samples_split=2,
|
||||
min_samples_leaf=1,
|
||||
min_weight_fraction_leaf=0.0,
|
||||
max_features="auto",
|
||||
max_leaf_nodes=None,
|
||||
bootstrap=True,
|
||||
oob_score=False,
|
||||
n_jobs=1,
|
||||
random_state=None,
|
||||
verbose=0,
|
||||
warm_start=False,
|
||||
):
|
||||
"""Initialize RandomForestQuantileRegressor class
|
||||
|
||||
Args:
|
||||
|
|
@ -271,16 +273,23 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor):
|
|||
super(RandomForestQuantileRegressor, self).__init__(
|
||||
base_estimator=DecisionTreeQuantileRegressor(),
|
||||
n_estimators=n_estimators,
|
||||
estimator_params=("criterion", "max_depth", "min_samples_split",
|
||||
"min_samples_leaf", "min_weight_fraction_leaf",
|
||||
"max_features", "max_leaf_nodes",
|
||||
"random_state"),
|
||||
estimator_params=(
|
||||
"criterion",
|
||||
"max_depth",
|
||||
"min_samples_split",
|
||||
"min_samples_leaf",
|
||||
"min_weight_fraction_leaf",
|
||||
"max_features",
|
||||
"max_leaf_nodes",
|
||||
"random_state",
|
||||
),
|
||||
bootstrap=bootstrap,
|
||||
oob_score=oob_score,
|
||||
n_jobs=n_jobs,
|
||||
random_state=random_state,
|
||||
verbose=verbose,
|
||||
warm_start=warm_start)
|
||||
warm_start=warm_start,
|
||||
)
|
||||
|
||||
self.criterion = criterion
|
||||
self.max_depth = max_depth
|
||||
|
|
@ -289,5 +298,3 @@ class RandomForestQuantileRegressor(BaseForestQuantileRegressor):
|
|||
self.min_weight_fraction_leaf = min_weight_fraction_leaf
|
||||
self.max_features = max_features
|
||||
self.max_leaf_nodes = max_leaf_nodes
|
||||
|
||||
|
||||
|
|
@ -3,6 +3,7 @@
|
|||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def weighted_percentile_vectorized(a, quantiles, weights=None, sorter=None):
|
||||
"""Returns the weighted percentile of a at q given weights.
|
||||
|
||||
|
|
@ -69,8 +70,7 @@ def weighted_percentile_vectorized(a, quantiles, weights=None, sorter=None):
|
|||
percentiles = np.zeros_like(quantiles)
|
||||
for i, q in enumerate(quantiles):
|
||||
if q > 100 or q < 0:
|
||||
raise ValueError("q should be in-between 0 and 100, "
|
||||
"got %d" % q)
|
||||
raise ValueError("q should be in-between 0 and 100, " "got %d" % q)
|
||||
|
||||
start = np.searchsorted(partial_sum, q) - 1
|
||||
if start == len(sorted_cum_weights) - 1:
|
||||
|
|
@ -4,9 +4,9 @@ all the modules in TSPerf. Each submission folder needs its own localpath.py fil
|
|||
"""
|
||||
|
||||
import os, sys
|
||||
|
||||
_CURR_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
TSPERF_DIR = os.path.dirname(os.path.dirname(os.path.dirname(_CURR_DIR)))
|
||||
|
||||
if TSPERF_DIR not in sys.path:
|
||||
sys.path.insert(0, TSPERF_DIR)
|
||||
|
||||
|
|
@ -9,16 +9,10 @@ from ensemble_parallel import RandomForestQuantileRegressor
|
|||
# get seed value
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--data-folder",
|
||||
type=str,
|
||||
dest="data_folder",
|
||||
help="data folder mounting point",
|
||||
"--data-folder", type=str, dest="data_folder", help="data folder mounting point",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output-folder",
|
||||
type=str,
|
||||
dest="output_folder",
|
||||
help="output folder mounting point",
|
||||
"--output-folder", type=str, dest="output_folder", help="output folder mounting point",
|
||||
)
|
||||
parser.add_argument("--seed", type=int, dest="seed", help="random seed")
|
||||
args = parser.parse_args()
|
||||
|
|
@ -27,9 +21,7 @@ args = parser.parse_args()
|
|||
data_dir = join(args.data_folder, "features")
|
||||
train_dir = join(data_dir, "train")
|
||||
test_dir = join(data_dir, "test")
|
||||
output_file = join(
|
||||
args.output_folder, "submission_seed_{}.csv".format(args.seed)
|
||||
)
|
||||
output_file = join(args.output_folder, "submission_seed_{}.csv".format(args.seed))
|
||||
|
||||
# do 6 rounds of forecasting, at each round output 9 quantiles
|
||||
n_rounds = 6
|
||||
|
|
@ -59,19 +51,13 @@ for i in range(1, n_rounds + 1):
|
|||
train_df_hour = pd.get_dummies(train_df_hour, columns=["Zone"])
|
||||
# remove column that are not useful (Datetime) or are not
|
||||
# available in the test set (DEMAND, DryBulb, DewPnt)
|
||||
X_train = train_df_hour.drop(
|
||||
columns=["Datetime", "DEMAND", "DryBulb", "DewPnt"]
|
||||
).values
|
||||
X_train = train_df_hour.drop(columns=["Datetime", "DEMAND", "DryBulb", "DewPnt"]).values
|
||||
|
||||
y_train = train_df_hour["DEMAND"].values
|
||||
|
||||
# train a model
|
||||
rfqr = RandomForestQuantileRegressor(
|
||||
random_state=args.seed,
|
||||
n_jobs=-1,
|
||||
n_estimators=1000,
|
||||
max_features="sqrt",
|
||||
max_depth=12,
|
||||
random_state=args.seed, n_jobs=-1, n_estimators=1000, max_features="sqrt", max_depth=12,
|
||||
)
|
||||
rfqr.fit(X_train, y_train)
|
||||
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Create input features for the Dilated Convolutional Neural Network (CNN) model.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# Append TSPerf path to sys.path
|
||||
tsperf_dir = "."
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
# Import TSPerf components
|
||||
from utils import *
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
|
||||
def make_features(pred_round, train_dir, pred_steps, offset, store_list, brand_list):
|
||||
"""Create a dataframe of the input features.
|
||||
|
||||
Args:
|
||||
pred_round (Integer): Prediction round
|
||||
train_dir (String): Path of the training data directory
|
||||
pred_steps (Integer): Number of prediction steps
|
||||
offset (Integer): Length of training data skipped in the retraining
|
||||
store_list (Numpy Array): List of all the store IDs
|
||||
brand_list (Numpy Array): List of all the brand IDs
|
||||
|
||||
Returns:
|
||||
data_filled (Dataframe): Dataframe including the input features
|
||||
data_scaled (Dataframe): Dataframe including the normalized features
|
||||
"""
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(train_dir, "train_round_" + str(pred_round + 1) + ".csv"))
|
||||
train_df["move"] = train_df["logmove"].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[["store", "brand", "week", "move"]]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
week_list = range(bs.TRAIN_START_WEEK + offset, bs.TEST_END_WEEK_LIST[pred_round] + 1)
|
||||
d = {"store": store_list, "brand": brand_list, "week": week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(train_dir, "aux_round_" + str(pred_round + 1) + ".csv"))
|
||||
data_filled = pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = [
|
||||
"price1",
|
||||
"price2",
|
||||
"price3",
|
||||
"price4",
|
||||
"price5",
|
||||
"price6",
|
||||
"price7",
|
||||
"price8",
|
||||
"price9",
|
||||
"price10",
|
||||
"price11",
|
||||
]
|
||||
data_filled["price"] = data_filled.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
data_filled["avg_price"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled["price_ratio"] = data_filled["price"] / data_filled["avg_price"]
|
||||
data_filled.drop(price_cols, axis=1, inplace=True)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: x.fillna(method="ffill").fillna(method="bfill")
|
||||
)
|
||||
|
||||
# Create datetime features
|
||||
data_filled["week_start"] = data_filled["week"].apply(
|
||||
lambda x: bs.FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7)
|
||||
)
|
||||
data_filled["month"] = data_filled["week_start"].apply(lambda x: x.month)
|
||||
data_filled["week_of_month"] = data_filled["week_start"].apply(lambda x: week_of_month(x))
|
||||
data_filled.drop("week_start", axis=1, inplace=True)
|
||||
|
||||
# Normalize the dataframe of features
|
||||
cols_normalize = data_filled.columns.difference(["store", "brand", "week"])
|
||||
data_scaled, min_max_scaler = normalize_dataframe(data_filled, cols_normalize)
|
||||
|
||||
return data_filled, data_scaled
|
||||
|
|
@ -0,0 +1,223 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Train and score a Dilated Convolutional Neural Network (CNN) model using Keras package with TensorFlow backend.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import keras
|
||||
import random
|
||||
import argparse
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import tensorflow as tf
|
||||
|
||||
from keras import optimizers
|
||||
from keras.layers import *
|
||||
from keras.models import Model, load_model
|
||||
from keras.callbacks import ModelCheckpoint
|
||||
|
||||
# Append TSPerf path to sys.path (assume we run the script from TSPerf directory)
|
||||
tsperf_dir = "."
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
# Import TSPerf components
|
||||
from utils import *
|
||||
from make_features import make_features
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
# Model definition
|
||||
def create_dcnn_model(seq_len, kernel_size=2, n_filters=3, n_input_series=1, n_outputs=1):
|
||||
"""Create a Dilated CNN model.
|
||||
|
||||
Args:
|
||||
seq_len (Integer): Input sequence length
|
||||
kernel_size (Integer): Kernel size of each convolutional layer
|
||||
n_filters (Integer): Number of filters in each convolutional layer
|
||||
n_outputs (Integer): Number of outputs in the last layer
|
||||
|
||||
Returns:
|
||||
Keras Model object
|
||||
"""
|
||||
# Sequential input
|
||||
seq_in = Input(shape=(seq_len, n_input_series))
|
||||
|
||||
# Categorical input
|
||||
cat_fea_in = Input(shape=(2,), dtype="uint8")
|
||||
store_id = Lambda(lambda x: x[:, 0, None])(cat_fea_in)
|
||||
brand_id = Lambda(lambda x: x[:, 1, None])(cat_fea_in)
|
||||
store_embed = Embedding(MAX_STORE_ID + 1, 7, input_length=1)(store_id)
|
||||
brand_embed = Embedding(MAX_BRAND_ID + 1, 4, input_length=1)(brand_id)
|
||||
|
||||
# Dilated convolutional layers
|
||||
c1 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=1, padding="causal", activation="relu")(
|
||||
seq_in
|
||||
)
|
||||
c2 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=2, padding="causal", activation="relu")(c1)
|
||||
c3 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=4, padding="causal", activation="relu")(c2)
|
||||
|
||||
# Skip connections
|
||||
c4 = concatenate([c1, c3])
|
||||
|
||||
# Output of convolutional layers
|
||||
conv_out = Conv1D(8, 1, activation="relu")(c4)
|
||||
conv_out = Dropout(args.dropout_rate)(conv_out)
|
||||
conv_out = Flatten()(conv_out)
|
||||
|
||||
# Concatenate with categorical features
|
||||
x = concatenate([conv_out, Flatten()(store_embed), Flatten()(brand_embed)])
|
||||
x = Dense(16, activation="relu")(x)
|
||||
output = Dense(n_outputs, activation="linear")(x)
|
||||
|
||||
# Define model interface, loss function, and optimizer
|
||||
model = Model(inputs=[seq_in, cat_fea_in], outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--seed", type=int, dest="seed", default=1, help="random seed")
|
||||
parser.add_argument("--seq-len", type=int, dest="seq_len", default=15, help="length of the input sequence")
|
||||
parser.add_argument("--dropout-rate", type=float, dest="dropout_rate", default=0.01, help="dropout ratio")
|
||||
parser.add_argument("--batch-size", type=int, dest="batch_size", default=64, help="mini batch size for training")
|
||||
parser.add_argument("--learning-rate", type=float, dest="learning_rate", default=0.015, help="learning rate")
|
||||
parser.add_argument("--epochs", type=int, dest="epochs", default=25, help="# of epochs")
|
||||
args = parser.parse_args()
|
||||
|
||||
# Fix random seeds
|
||||
np.random.seed(args.seed)
|
||||
random.seed(args.seed)
|
||||
tf.set_random_seed(args.seed)
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = os.path.join(tsperf_dir, "retail_sales", "OrangeJuice_Pt_3Weeks_Weekly", "data")
|
||||
SUBMISSION_DIR = os.path.join(
|
||||
tsperf_dir, "retail_sales", "OrangeJuice_Pt_3Weeks_Weekly", "submissions", "DilatedCNN"
|
||||
)
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, "train")
|
||||
|
||||
# Dataset parameters
|
||||
MAX_STORE_ID = 137
|
||||
MAX_BRAND_ID = 11
|
||||
|
||||
# Parameters of the model
|
||||
PRED_HORIZON = 3
|
||||
PRED_STEPS = 2
|
||||
SEQ_LEN = args.seq_len
|
||||
DYNAMIC_FEATURES = ["deal", "feat", "month", "week_of_month", "price", "price_ratio"]
|
||||
STATIC_FEATURES = ["store", "brand"]
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, "train_round_1.csv"))
|
||||
store_list = train_df["store"].unique()
|
||||
brand_list = train_df["brand"].unique()
|
||||
store_brand = [(x, y) for x in store_list for y in brand_list]
|
||||
|
||||
# Train and predict for all forecast rounds
|
||||
pred_all = []
|
||||
file_name = os.path.join(SUBMISSION_DIR, "dcnn_model.h5")
|
||||
for r in range(bs.NUM_ROUNDS):
|
||||
print("---- Round " + str(r + 1) + " ----")
|
||||
offset = 0 if r == 0 else 40 + r * PRED_STEPS
|
||||
# Create features
|
||||
data_filled, data_scaled = make_features(r, TRAIN_DIR, PRED_STEPS, offset, store_list, brand_list)
|
||||
|
||||
# Create sequence array for 'move'
|
||||
start_timestep = 0
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK - PRED_HORIZON
|
||||
train_input1 = gen_sequence_array(
|
||||
data_scaled, store_brand, SEQ_LEN, ["move"], start_timestep, end_timestep - offset
|
||||
)
|
||||
|
||||
# Create sequence array for other dynamic features
|
||||
start_timestep = PRED_HORIZON
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK
|
||||
train_input2 = gen_sequence_array(
|
||||
data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep, end_timestep - offset
|
||||
)
|
||||
|
||||
seq_in = np.concatenate([train_input1, train_input2], axis=2)
|
||||
|
||||
# Create array of static features
|
||||
total_timesteps = bs.TRAIN_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK - SEQ_LEN - PRED_HORIZON + 2
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps - offset, STATIC_FEATURES)
|
||||
|
||||
# Create training output
|
||||
start_timestep = SEQ_LEN + PRED_HORIZON - PRED_STEPS
|
||||
end_timestep = bs.TRAIN_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK
|
||||
train_output = gen_sequence_array(
|
||||
data_filled, store_brand, PRED_STEPS, ["move"], start_timestep, end_timestep - offset
|
||||
)
|
||||
train_output = np.squeeze(train_output)
|
||||
|
||||
# Create and train model
|
||||
if r == 0:
|
||||
model = create_dcnn_model(
|
||||
seq_len=SEQ_LEN, n_filters=2, n_input_series=1 + len(DYNAMIC_FEATURES), n_outputs=PRED_STEPS
|
||||
)
|
||||
adam = optimizers.Adam(lr=args.learning_rate)
|
||||
model.compile(loss="mape", optimizer=adam, metrics=["mape"])
|
||||
# Define checkpoint and fit model
|
||||
checkpoint = ModelCheckpoint(file_name, monitor="loss", save_best_only=True, mode="min", verbose=0)
|
||||
callbacks_list = [checkpoint]
|
||||
history = model.fit(
|
||||
[seq_in, cat_fea_in],
|
||||
train_output,
|
||||
epochs=args.epochs,
|
||||
batch_size=args.batch_size,
|
||||
callbacks=callbacks_list,
|
||||
verbose=0,
|
||||
)
|
||||
else:
|
||||
model = load_model(file_name)
|
||||
checkpoint = ModelCheckpoint(file_name, monitor="loss", save_best_only=True, mode="min", verbose=0)
|
||||
callbacks_list = [checkpoint]
|
||||
history = model.fit(
|
||||
[seq_in, cat_fea_in],
|
||||
train_output,
|
||||
epochs=1,
|
||||
batch_size=args.batch_size,
|
||||
callbacks=callbacks_list,
|
||||
verbose=0,
|
||||
)
|
||||
|
||||
# Get inputs for prediction
|
||||
start_timestep = bs.TEST_START_WEEK_LIST[r] - bs.TRAIN_START_WEEK - SEQ_LEN - PRED_HORIZON + PRED_STEPS
|
||||
end_timestep = bs.TEST_START_WEEK_LIST[r] - bs.TRAIN_START_WEEK + PRED_STEPS - 1 - PRED_HORIZON
|
||||
test_input1 = gen_sequence_array(
|
||||
data_scaled, store_brand, SEQ_LEN, ["move"], start_timestep - offset, end_timestep - offset
|
||||
)
|
||||
|
||||
start_timestep = bs.TEST_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK - SEQ_LEN + 1
|
||||
end_timestep = bs.TEST_END_WEEK_LIST[r] - bs.TRAIN_START_WEEK
|
||||
test_input2 = gen_sequence_array(
|
||||
data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep - offset, end_timestep - offset
|
||||
)
|
||||
|
||||
seq_in = np.concatenate([test_input1, test_input2], axis=2)
|
||||
|
||||
total_timesteps = 1
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps, STATIC_FEATURES)
|
||||
|
||||
# Make prediction
|
||||
pred = np.round(model.predict([seq_in, cat_fea_in]))
|
||||
|
||||
# Create dataframe for submission
|
||||
exp_output = data_filled[data_filled.week >= bs.TEST_START_WEEK_LIST[r]].reset_index(drop=True)
|
||||
exp_output = exp_output[["store", "brand", "week"]]
|
||||
pred_df = (
|
||||
exp_output.sort_values(["store", "brand", "week"]).loc[:, ["store", "brand", "week"]].reset_index(drop=True)
|
||||
)
|
||||
pred_df["weeks_ahead"] = pred_df["week"] - bs.TRAIN_END_WEEK_LIST[r]
|
||||
pred_df["round"] = r + 1
|
||||
pred_df["prediction"] = np.reshape(pred, (pred.size, 1))
|
||||
pred_all.append(pred_df)
|
||||
|
||||
# Generate submission
|
||||
submission = pd.concat(pred_all, axis=0).reset_index(drop=True)
|
||||
submission = submission[["round", "store", "brand", "week", "weeks_ahead", "prediction"]]
|
||||
filename = "submission_seed_" + str(args.seed) + ".csv"
|
||||
submission.to_csv(os.path.join(SUBMISSION_DIR, filename), index=False)
|
||||
print("Done")
|
||||
|
|
@ -0,0 +1,212 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Perform cross validation of a Dilated Convolutional Neural Network (CNN) model on the training data of the 1st forecast round.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import keras
|
||||
import argparse
|
||||
import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from utils import *
|
||||
from keras.layers import *
|
||||
from keras.models import Model
|
||||
from keras import optimizers
|
||||
from keras.utils import multi_gpu_model
|
||||
from azureml.core import Run
|
||||
|
||||
# Model definition
|
||||
def create_dcnn_model(seq_len, kernel_size=2, n_filters=3, n_input_series=1, n_outputs=1):
|
||||
"""Create a Dilated CNN model.
|
||||
|
||||
Args:
|
||||
seq_len (Integer): Input sequence length
|
||||
kernel_size (Integer): Kernel size of each convolutional layer
|
||||
n_filters (Integer): Number of filters in each convolutional layer
|
||||
n_outputs (Integer): Number of outputs in the last layer
|
||||
|
||||
Returns:
|
||||
Keras Model object
|
||||
"""
|
||||
# Sequential input
|
||||
seq_in = Input(shape=(seq_len, n_input_series))
|
||||
|
||||
# Categorical input
|
||||
cat_fea_in = Input(shape=(2,), dtype="uint8")
|
||||
store_id = Lambda(lambda x: x[:, 0, None])(cat_fea_in)
|
||||
brand_id = Lambda(lambda x: x[:, 1, None])(cat_fea_in)
|
||||
store_embed = Embedding(MAX_STORE_ID + 1, 7, input_length=1)(store_id)
|
||||
brand_embed = Embedding(MAX_BRAND_ID + 1, 4, input_length=1)(brand_id)
|
||||
|
||||
# Dilated convolutional layers
|
||||
c1 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=1, padding="causal", activation="relu")(
|
||||
seq_in
|
||||
)
|
||||
c2 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=2, padding="causal", activation="relu")(c1)
|
||||
c3 = Conv1D(filters=n_filters, kernel_size=kernel_size, dilation_rate=4, padding="causal", activation="relu")(c2)
|
||||
|
||||
# Skip connections
|
||||
c4 = concatenate([c1, c3])
|
||||
|
||||
# Output of convolutional layers
|
||||
conv_out = Conv1D(8, 1, activation="relu")(c4)
|
||||
conv_out = Dropout(args.dropout_rate)(conv_out)
|
||||
conv_out = Flatten()(conv_out)
|
||||
|
||||
# Concatenate with categorical features
|
||||
x = concatenate([conv_out, Flatten()(store_embed), Flatten()(brand_embed)])
|
||||
x = Dense(16, activation="relu")(x)
|
||||
output = Dense(n_outputs, activation="linear")(x)
|
||||
|
||||
# Define model interface, loss function, and optimizer
|
||||
model = Model(inputs=[seq_in, cat_fea_in], outputs=output)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-folder", type=str, dest="data_folder", help="data folder mounting point")
|
||||
parser.add_argument("--seq-len", type=int, dest="seq_len", default=20, help="length of the input sequence")
|
||||
parser.add_argument("--batch-size", type=int, dest="batch_size", default=64, help="mini batch size for training")
|
||||
parser.add_argument("--dropout-rate", type=float, dest="dropout_rate", default=0.10, help="dropout ratio")
|
||||
parser.add_argument("--learning-rate", type=float, dest="learning_rate", default=0.01, help="learning rate")
|
||||
parser.add_argument("--epochs", type=int, dest="epochs", default=30, help="# of epochs")
|
||||
args = parser.parse_args()
|
||||
args.dropout_rate = round(args.dropout_rate, 2)
|
||||
print(args)
|
||||
|
||||
# Start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = args.data_folder
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, "train")
|
||||
|
||||
# Data and forecast problem parameters
|
||||
MAX_STORE_ID = 137
|
||||
MAX_BRAND_ID = 11
|
||||
PRED_HORIZON = 3
|
||||
PRED_STEPS = 2
|
||||
TRAIN_START_WEEK = 40
|
||||
TRAIN_END_WEEK_LIST = list(range(135, 159, 2))
|
||||
TEST_START_WEEK_LIST = list(range(137, 161, 2))
|
||||
TEST_END_WEEK_LIST = list(range(138, 162, 2))
|
||||
# The start datetime of the first week in the record
|
||||
FIRST_WEEK_START = pd.to_datetime("1989-09-14 00:00:00")
|
||||
|
||||
# Input sequence length and feature names
|
||||
SEQ_LEN = args.seq_len
|
||||
DYNAMIC_FEATURES = ["deal", "feat", "month", "week_of_month", "price", "price_ratio"]
|
||||
STATIC_FEATURES = ["store", "brand"]
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, "train_round_1.csv"))
|
||||
store_list = train_df["store"].unique()
|
||||
brand_list = train_df["brand"].unique()
|
||||
store_brand = [(x, y) for x in store_list for y in brand_list]
|
||||
|
||||
# Train and validate the model using only the first round data
|
||||
r = 0
|
||||
print("---- Round " + str(r + 1) + " ----")
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, "train_round_" + str(r + 1) + ".csv"))
|
||||
train_df["move"] = train_df["logmove"].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[["store", "brand", "week", "move"]]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
week_list = range(TRAIN_START_WEEK, TEST_END_WEEK_LIST[r] + 1)
|
||||
d = {"store": store_list, "brand": brand_list, "week": week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(TRAIN_DIR, "aux_round_" + str(r + 1) + ".csv"))
|
||||
data_filled = pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = [
|
||||
"price1",
|
||||
"price2",
|
||||
"price3",
|
||||
"price4",
|
||||
"price5",
|
||||
"price6",
|
||||
"price7",
|
||||
"price8",
|
||||
"price9",
|
||||
"price10",
|
||||
"price11",
|
||||
]
|
||||
data_filled["price"] = data_filled.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
data_filled["avg_price"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled["price_ratio"] = data_filled.apply(lambda x: x["price"] / x["avg_price"], axis=1)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: x.fillna(method="ffill").fillna(method="bfill")
|
||||
)
|
||||
|
||||
# Create datetime features
|
||||
data_filled["week_start"] = data_filled["week"].apply(
|
||||
lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7)
|
||||
)
|
||||
data_filled["day"] = data_filled["week_start"].apply(lambda x: x.day)
|
||||
data_filled["week_of_month"] = data_filled["week_start"].apply(lambda x: week_of_month(x))
|
||||
data_filled["month"] = data_filled["week_start"].apply(lambda x: x.month)
|
||||
data_filled.drop("week_start", axis=1, inplace=True)
|
||||
|
||||
# Normalize the dataframe of features
|
||||
cols_normalize = data_filled.columns.difference(["store", "brand", "week"])
|
||||
data_scaled, min_max_scaler = normalize_dataframe(data_filled, cols_normalize)
|
||||
|
||||
# Create sequence array for 'move'
|
||||
start_timestep = 0
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r] - TRAIN_START_WEEK - PRED_HORIZON
|
||||
train_input1 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, ["move"], start_timestep, end_timestep)
|
||||
|
||||
# Create sequence array for other dynamic features
|
||||
start_timestep = PRED_HORIZON
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r] - TRAIN_START_WEEK
|
||||
train_input2 = gen_sequence_array(data_scaled, store_brand, SEQ_LEN, DYNAMIC_FEATURES, start_timestep, end_timestep)
|
||||
|
||||
seq_in = np.concatenate((train_input1, train_input2), axis=2)
|
||||
|
||||
# Create array of static features
|
||||
total_timesteps = TRAIN_END_WEEK_LIST[r] - TRAIN_START_WEEK - SEQ_LEN - PRED_HORIZON + 2
|
||||
cat_fea_in = static_feature_array(data_filled, total_timesteps, STATIC_FEATURES)
|
||||
|
||||
# Create training output
|
||||
start_timestep = SEQ_LEN + PRED_HORIZON - PRED_STEPS
|
||||
end_timestep = TRAIN_END_WEEK_LIST[r] - TRAIN_START_WEEK
|
||||
train_output = gen_sequence_array(data_filled, store_brand, PRED_STEPS, ["move"], start_timestep, end_timestep)
|
||||
train_output = np.squeeze(train_output)
|
||||
|
||||
# Create model
|
||||
model = create_dcnn_model(
|
||||
seq_len=SEQ_LEN, n_filters=2, n_input_series=1 + len(DYNAMIC_FEATURES), n_outputs=PRED_STEPS
|
||||
)
|
||||
|
||||
# Convert to GPU model
|
||||
try:
|
||||
model = multi_gpu_model(model)
|
||||
print("Training using multiple GPUs...")
|
||||
except:
|
||||
print("Training using single GPU or CPU...")
|
||||
|
||||
adam = optimizers.Adam(lr=args.learning_rate)
|
||||
model.compile(loss="mape", optimizer=adam, metrics=["mape", "mae"])
|
||||
|
||||
# Model training and validation
|
||||
history = model.fit(
|
||||
[seq_in, cat_fea_in], train_output, epochs=args.epochs, batch_size=args.batch_size, validation_split=0.05
|
||||
)
|
||||
val_loss = history.history["val_loss"][-1]
|
||||
print("Validation loss is {}".format(val_loss))
|
||||
|
||||
# Log the validation loss/MAPE
|
||||
run.log("MAPE", np.float(val_loss))
|
||||
|
|
@ -1,11 +1,12 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Utility functions for building the Dilated Convolutional Neural Network (CNN) model.
|
||||
# Utility functions for building the Dilated Convolutional Neural Network (CNN) model.
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
|
||||
|
||||
def week_of_month(dt):
|
||||
"""Get the week of the month for the specified date.
|
||||
|
||||
|
|
@ -14,14 +15,16 @@ def week_of_month(dt):
|
|||
|
||||
Returns:
|
||||
wom (Integer): Week of the month of the input date
|
||||
"""
|
||||
"""
|
||||
from math import ceil
|
||||
|
||||
first_day = dt.replace(day=1)
|
||||
dom = dt.day
|
||||
adjusted_dom = dom + first_day.weekday()
|
||||
wom = int(ceil(adjusted_dom/7.0))
|
||||
wom = int(ceil(adjusted_dom / 7.0))
|
||||
return wom
|
||||
|
||||
|
||||
def df_from_cartesian_product(dict_in):
|
||||
"""Generate a Pandas dataframe from Cartesian product of lists.
|
||||
|
||||
|
|
@ -33,11 +36,13 @@ def df_from_cartesian_product(dict_in):
|
|||
"""
|
||||
from collections import OrderedDict
|
||||
from itertools import product
|
||||
|
||||
od = OrderedDict(sorted(dict_in.items()))
|
||||
cart = list(product(*od.values()))
|
||||
df = pd.DataFrame(cart, columns=od.keys())
|
||||
return df
|
||||
|
||||
|
||||
def gen_sequence(df, seq_len, seq_cols, start_timestep=0, end_timestep=None):
|
||||
"""Reshape features into an array of dimension (time steps, features).
|
||||
|
||||
|
|
@ -54,9 +59,12 @@ def gen_sequence(df, seq_len, seq_cols, start_timestep=0, end_timestep=None):
|
|||
data_array = df[seq_cols].values
|
||||
if end_timestep is None:
|
||||
end_timestep = df.shape[0]
|
||||
for start, stop in zip(range(start_timestep, end_timestep-seq_len+2), range(start_timestep+seq_len, end_timestep+2)):
|
||||
for start, stop in zip(
|
||||
range(start_timestep, end_timestep - seq_len + 2), range(start_timestep + seq_len, end_timestep + 2)
|
||||
):
|
||||
yield data_array[start:stop, :]
|
||||
|
||||
|
||||
def gen_sequence_array(df_all, store_brand, seq_len, seq_cols, start_timestep=0, end_timestep=None):
|
||||
"""Combine feature sequences for all the combinations of (store, brand) into an 3d array.
|
||||
|
||||
|
|
@ -70,11 +78,22 @@ def gen_sequence_array(df_all, store_brand, seq_len, seq_cols, start_timestep=0,
|
|||
Returns:
|
||||
seq_array (Numpy Array): An array of the feature sequences of all stores and brands
|
||||
"""
|
||||
seq_gen = (list(gen_sequence(df_all[(df_all['store']==cur_store) & (df_all['brand']==cur_brand)], seq_len, seq_cols, start_timestep, end_timestep)) \
|
||||
for cur_store, cur_brand in store_brand)
|
||||
seq_gen = (
|
||||
list(
|
||||
gen_sequence(
|
||||
df_all[(df_all["store"] == cur_store) & (df_all["brand"] == cur_brand)],
|
||||
seq_len,
|
||||
seq_cols,
|
||||
start_timestep,
|
||||
end_timestep,
|
||||
)
|
||||
)
|
||||
for cur_store, cur_brand in store_brand
|
||||
)
|
||||
seq_array = np.concatenate(list(seq_gen)).astype(np.float32)
|
||||
return seq_array
|
||||
|
||||
|
||||
def static_feature_array(df_all, total_timesteps, seq_cols):
|
||||
"""Generate an array which encodes all the static features.
|
||||
|
||||
|
|
@ -86,10 +105,11 @@ def static_feature_array(df_all, total_timesteps, seq_cols):
|
|||
Return:
|
||||
fea_array (Numpy Array): An array of static features of all stores and brands
|
||||
"""
|
||||
fea_df = df_all.groupby(['store', 'brand']).apply(lambda x: x.iloc[:total_timesteps,:]).reset_index(drop=True)
|
||||
fea_df = df_all.groupby(["store", "brand"]).apply(lambda x: x.iloc[:total_timesteps, :]).reset_index(drop=True)
|
||||
fea_array = fea_df[seq_cols].values
|
||||
return fea_array
|
||||
|
||||
|
||||
def normalize_dataframe(df, seq_cols, scaler=MinMaxScaler()):
|
||||
"""Normalize a subset of columns of a dataframe.
|
||||
|
||||
|
|
@ -102,7 +122,6 @@ def normalize_dataframe(df, seq_cols, scaler=MinMaxScaler()):
|
|||
df_scaled (Dataframe): Normalized dataframe
|
||||
"""
|
||||
cols_fixed = df.columns.difference(seq_cols)
|
||||
df_scaled = pd.DataFrame(scaler.fit_transform(df[seq_cols]),
|
||||
columns=seq_cols, index=df.index)
|
||||
df_scaled = pd.DataFrame(scaler.fit_transform(df[seq_cols]), columns=seq_cols, index=df.index)
|
||||
df_scaled = pd.concat([df[cols_fixed], df_scaled], axis=1)
|
||||
return df_scaled, scaler
|
||||
return df_scaled, scaler
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Create input features for the boosted decision tree model.
|
||||
# Create input features for the boosted decision tree model.
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
|
@ -9,9 +9,9 @@ import itertools
|
|||
import datetime
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
import lightgbm as lgb
|
||||
|
||||
# Append TSPerf path to sys.path
|
||||
# Append TSPerf path to sys.path
|
||||
tsperf_dir = os.getcwd()
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
|
@ -20,6 +20,7 @@ if tsperf_dir not in sys.path:
|
|||
from utils import *
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
|
||||
def lagged_features(df, lags):
|
||||
"""Create lagged features based on time series data.
|
||||
|
||||
|
|
@ -33,11 +34,12 @@ def lagged_features(df, lags):
|
|||
df_list = []
|
||||
for lag in lags:
|
||||
df_shifted = df.shift(lag)
|
||||
df_shifted.columns = [x + '_lag' + str(lag) for x in df_shifted.columns]
|
||||
df_shifted.columns = [x + "_lag" + str(lag) for x in df_shifted.columns]
|
||||
df_list.append(df_shifted)
|
||||
fea = pd.concat(df_list, axis=1)
|
||||
return fea
|
||||
|
||||
|
||||
def moving_averages(df, start_step, window_size=None):
|
||||
"""Compute averages of every feature over moving time windows.
|
||||
|
||||
|
|
@ -49,12 +51,13 @@ def moving_averages(df, start_step, window_size=None):
|
|||
Returns:
|
||||
fea (Dataframe): Dataframe consisting of the moving averages
|
||||
"""
|
||||
if window_size == None: # Use a large window to compute average over all historical data
|
||||
if window_size == None: # Use a large window to compute average over all historical data
|
||||
window_size = df.shape[0]
|
||||
fea = df.shift(start_step).rolling(min_periods=1, center=False, window=window_size).mean()
|
||||
fea.columns = fea.columns + '_mean'
|
||||
fea.columns = fea.columns + "_mean"
|
||||
return fea
|
||||
|
||||
|
||||
def combine_features(df, lag_fea, lags, window_size, used_columns):
|
||||
"""Combine different features for a certain store-brand.
|
||||
|
||||
|
|
@ -73,6 +76,7 @@ def combine_features(df, lag_fea, lags, window_size, used_columns):
|
|||
fea_all = pd.concat([df[used_columns], lagged_fea, moving_avg], axis=1)
|
||||
return fea_all
|
||||
|
||||
|
||||
def make_features(pred_round, train_dir, lags, window_size, offset, used_columns, store_list, brand_list):
|
||||
"""Create a dataframe of the input features.
|
||||
|
||||
|
|
@ -88,46 +92,59 @@ def make_features(pred_round, train_dir, lags, window_size, offset, used_columns
|
|||
|
||||
Returns:
|
||||
features (Dataframe): Dataframe including all the input features and target variable
|
||||
"""
|
||||
"""
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(train_dir, 'train_round_'+str(pred_round+1)+'.csv'))
|
||||
train_df['move'] = train_df['logmove'].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[['store', 'brand', 'week', 'move']]
|
||||
train_df = pd.read_csv(os.path.join(train_dir, "train_round_" + str(pred_round + 1) + ".csv"))
|
||||
train_df["move"] = train_df["logmove"].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[["store", "brand", "week", "move"]]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
week_list = range(bs.TRAIN_START_WEEK + offset, bs.TEST_END_WEEK_LIST[pred_round]+1)
|
||||
d = {'store': store_list,
|
||||
'brand': brand_list,
|
||||
'week': week_list}
|
||||
week_list = range(bs.TRAIN_START_WEEK + offset, bs.TEST_END_WEEK_LIST[pred_round] + 1)
|
||||
d = {"store": store_list, "brand": brand_list, "week": week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
data_filled = pd.merge(data_grid, train_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(train_dir, 'aux_round_'+str(pred_round+1)+'.csv'))
|
||||
data_filled = pd.merge(data_filled, aux_df, how='left',
|
||||
on=['store', 'brand', 'week'])
|
||||
|
||||
aux_df = pd.read_csv(os.path.join(train_dir, "aux_round_" + str(pred_round + 1) + ".csv"))
|
||||
data_filled = pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = ['price1', 'price2', 'price3', 'price4', 'price5', 'price6', 'price7', 'price8', \
|
||||
'price9', 'price10', 'price11']
|
||||
data_filled['price'] = data_filled.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
|
||||
data_filled['avg_price'] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled['price_ratio'] = data_filled['price'] / data_filled['avg_price']
|
||||
data_filled.drop(price_cols, axis=1, inplace=True)
|
||||
price_cols = [
|
||||
"price1",
|
||||
"price2",
|
||||
"price3",
|
||||
"price4",
|
||||
"price5",
|
||||
"price6",
|
||||
"price7",
|
||||
"price8",
|
||||
"price9",
|
||||
"price10",
|
||||
"price11",
|
||||
]
|
||||
data_filled["price"] = data_filled.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
data_filled["avg_price"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled["price_ratio"] = data_filled["price"] / data_filled["avg_price"]
|
||||
data_filled.drop(price_cols, axis=1, inplace=True)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(['store', 'brand']).apply(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
|
||||
data_filled = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: x.fillna(method="ffill").fillna(method="bfill")
|
||||
)
|
||||
|
||||
# Create datetime features
|
||||
data_filled['week_start'] = data_filled['week'].apply(lambda x: bs.FIRST_WEEK_START + datetime.timedelta(days=(x-1)*7))
|
||||
data_filled['year'] = data_filled['week_start'].apply(lambda x: x.year)
|
||||
data_filled['month'] = data_filled['week_start'].apply(lambda x: x.month)
|
||||
data_filled['week_of_month'] = data_filled['week_start'].apply(lambda x: week_of_month(x))
|
||||
data_filled['day'] = data_filled['week_start'].apply(lambda x: x.day)
|
||||
data_filled.drop('week_start', axis=1, inplace=True)
|
||||
data_filled["week_start"] = data_filled["week"].apply(
|
||||
lambda x: bs.FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7)
|
||||
)
|
||||
data_filled["year"] = data_filled["week_start"].apply(lambda x: x.year)
|
||||
data_filled["month"] = data_filled["week_start"].apply(lambda x: x.month)
|
||||
data_filled["week_of_month"] = data_filled["week_start"].apply(lambda x: week_of_month(x))
|
||||
data_filled["day"] = data_filled["week_start"].apply(lambda x: x.day)
|
||||
data_filled.drop("week_start", axis=1, inplace=True)
|
||||
|
||||
# Create other features (lagged features, moving averages, etc.)
|
||||
features = data_filled.groupby(['store','brand']).apply(lambda x: combine_features(x, ['move'], lags, window_size, used_columns))
|
||||
features = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: combine_features(x, ["move"], lags, window_size, used_columns)
|
||||
)
|
||||
|
||||
return features
|
||||
return features
|
||||
|
|
@ -21,15 +21,12 @@ if tsperf_dir not in sys.path:
|
|||
|
||||
# Import TSPerf components
|
||||
from utils import df_from_cartesian_product
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings \
|
||||
as bs
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
pd.set_option("display.max_columns", None)
|
||||
|
||||
|
||||
def oj_preprocess(
|
||||
df, aux_df, week_list, store_list, brand_list, train_df=None
|
||||
):
|
||||
def oj_preprocess(df, aux_df, week_list, store_list, brand_list, train_df=None):
|
||||
|
||||
df["move"] = df["logmove"].apply(lambda x: round(math.exp(x)))
|
||||
df = df[["store", "brand", "week", "move"]].copy()
|
||||
|
|
@ -37,14 +34,10 @@ def oj_preprocess(
|
|||
# Create a dataframe to hold all necessary data
|
||||
d = {"store": store_list, "brand": brand_list, "week": week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(
|
||||
data_grid, df, how="left", on=["store", "brand", "week"]
|
||||
)
|
||||
data_filled = pd.merge(data_grid, df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
data_filled = pd.merge(
|
||||
data_filled, aux_df, how="left", on=["store", "brand", "week"]
|
||||
)
|
||||
data_filled = pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Fill missing values
|
||||
if train_df is not None:
|
||||
|
|
@ -60,22 +53,13 @@ def oj_preprocess(
|
|||
)
|
||||
|
||||
if train_df is not None:
|
||||
data_filled = data_filled.loc[
|
||||
data_filled["week_start"] > forecast_creation_time
|
||||
].copy()
|
||||
data_filled = data_filled.loc[data_filled["week_start"] > forecast_creation_time].copy()
|
||||
|
||||
return data_filled
|
||||
|
||||
|
||||
def make_features(
|
||||
pred_round,
|
||||
train_dir,
|
||||
lags,
|
||||
window_size,
|
||||
offset,
|
||||
used_columns,
|
||||
store_list,
|
||||
brand_list,
|
||||
pred_round, train_dir, lags, window_size, offset, used_columns, store_list, brand_list,
|
||||
):
|
||||
"""Create a dataframe of the input features.
|
||||
|
||||
|
|
@ -95,19 +79,11 @@ def make_features(
|
|||
target variable
|
||||
"""
|
||||
# Load training data
|
||||
train_df = pd.read_csv(
|
||||
os.path.join(train_dir, "train_round_" + str(pred_round + 1) + ".csv")
|
||||
)
|
||||
aux_df = pd.read_csv(
|
||||
os.path.join(train_dir, "aux_round_" + str(pred_round + 1) + ".csv")
|
||||
)
|
||||
week_list = range(
|
||||
bs.TRAIN_START_WEEK + offset, bs.TEST_END_WEEK_LIST[pred_round] + 1
|
||||
)
|
||||
train_df = pd.read_csv(os.path.join(train_dir, "train_round_" + str(pred_round + 1) + ".csv"))
|
||||
aux_df = pd.read_csv(os.path.join(train_dir, "aux_round_" + str(pred_round + 1) + ".csv"))
|
||||
week_list = range(bs.TRAIN_START_WEEK + offset, bs.TEST_END_WEEK_LIST[pred_round] + 1)
|
||||
|
||||
train_df_preprocessed = oj_preprocess(
|
||||
train_df, aux_df, week_list, store_list, brand_list
|
||||
)
|
||||
train_df_preprocessed = oj_preprocess(train_df, aux_df, week_list, store_list, brand_list)
|
||||
|
||||
df_config = {
|
||||
"time_col_name": "week_start",
|
||||
|
|
@ -117,9 +93,7 @@ def make_features(
|
|||
"time_format": "%Y-%m-%d",
|
||||
}
|
||||
|
||||
temporal_featurizer = TemporalFeaturizer(
|
||||
df_config=df_config, feature_list=["month_of_year", "week_of_month"]
|
||||
)
|
||||
temporal_featurizer = TemporalFeaturizer(df_config=df_config, feature_list=["month_of_year", "week_of_month"])
|
||||
|
||||
popularity_featurizer = PopularityFeaturizer(
|
||||
df_config=df_config,
|
||||
|
|
@ -143,12 +117,7 @@ def make_features(
|
|||
return_feature_col=True,
|
||||
)
|
||||
|
||||
lag_featurizer = LagFeaturizer(
|
||||
df_config=df_config,
|
||||
input_col_names="move",
|
||||
lags=lags,
|
||||
future_value_available=True,
|
||||
)
|
||||
lag_featurizer = LagFeaturizer(df_config=df_config, input_col_names="move", lags=lags, future_value_available=True,)
|
||||
moving_average_featurizer = RollingWindowFeaturizer(
|
||||
df_config=df_config,
|
||||
input_col_names="move",
|
||||
|
|
@ -0,0 +1,137 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Train and score a boosted decision tree model using [LightGBM Python package](https://github.com/Microsoft/LightGBM) from Microsoft,
|
||||
# which is a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
# Append TSPerf path to sys.path
|
||||
tsperf_dir = os.getcwd()
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
from make_features import make_features
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
|
||||
def make_predictions(df, model):
|
||||
"""Predict sales with the trained GBM model.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Dataframe including all needed features
|
||||
model (Model): Trained GBM model
|
||||
|
||||
Returns:
|
||||
Dataframe including the predicted sales of every store-brand
|
||||
"""
|
||||
predictions = pd.DataFrame({"move": model.predict(df.drop("move", axis=1))})
|
||||
predictions["move"] = predictions["move"].apply(lambda x: round(x))
|
||||
return pd.concat([df[["brand", "store", "week"]].reset_index(drop=True), predictions], axis=1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--seed", type=int, dest="seed", default=1, help="Random seed of GBM model")
|
||||
parser.add_argument("--num-leaves", type=int, dest="num_leaves", default=124, help="# of leaves of the tree")
|
||||
parser.add_argument(
|
||||
"--min-data-in-leaf", type=int, dest="min_data_in_leaf", default=340, help="minimum # of samples in each leaf"
|
||||
)
|
||||
parser.add_argument("--learning-rate", type=float, dest="learning_rate", default=0.1, help="learning rate")
|
||||
parser.add_argument(
|
||||
"--feature-fraction",
|
||||
type=float,
|
||||
dest="feature_fraction",
|
||||
default=0.65,
|
||||
help="ratio of features used in each iteration",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--bagging-fraction",
|
||||
type=float,
|
||||
dest="bagging_fraction",
|
||||
default=0.87,
|
||||
help="ratio of samples used in each iteration",
|
||||
)
|
||||
parser.add_argument("--bagging-freq", type=int, dest="bagging_freq", default=19, help="bagging frequency")
|
||||
parser.add_argument("--max-rounds", type=int, dest="max_rounds", default=940, help="# of boosting iterations")
|
||||
parser.add_argument("--max-lag", type=int, dest="max_lag", default=19, help="max lag of unit sales")
|
||||
parser.add_argument(
|
||||
"--window-size", type=int, dest="window_size", default=40, help="window size of moving average of unit sales"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
print(args)
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = os.path.join(tsperf_dir, "retail_sales", "OrangeJuice_Pt_3Weeks_Weekly", "data")
|
||||
SUBMISSION_DIR = os.path.join(tsperf_dir, "retail_sales", "OrangeJuice_Pt_3Weeks_Weekly", "submissions", "LightGBM")
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, "train")
|
||||
|
||||
# Parameters of GBM model
|
||||
params = {
|
||||
"objective": "mape",
|
||||
"num_leaves": args.num_leaves,
|
||||
"min_data_in_leaf": args.min_data_in_leaf,
|
||||
"learning_rate": args.learning_rate,
|
||||
"feature_fraction": args.feature_fraction,
|
||||
"bagging_fraction": args.bagging_fraction,
|
||||
"bagging_freq": args.bagging_freq,
|
||||
"num_rounds": args.max_rounds,
|
||||
"early_stopping_rounds": 125,
|
||||
"num_threads": 4,
|
||||
"seed": args.seed,
|
||||
}
|
||||
|
||||
# Lags and categorical features
|
||||
lags = np.arange(2, args.max_lag + 1)
|
||||
used_columns = ["store", "brand", "week", "week_of_month", "month", "deal", "feat", "move", "price", "price_ratio"]
|
||||
categ_fea = ["store", "brand", "deal"]
|
||||
|
||||
# Get unique stores and brands
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, "train_round_1.csv"))
|
||||
store_list = train_df["store"].unique()
|
||||
brand_list = train_df["brand"].unique()
|
||||
|
||||
# Train and predict for all forecast rounds
|
||||
pred_all = []
|
||||
metric_all = []
|
||||
for r in range(bs.NUM_ROUNDS):
|
||||
print("---- Round " + str(r + 1) + " ----")
|
||||
# Create features
|
||||
features = make_features(r, TRAIN_DIR, lags, args.window_size, 0, used_columns, store_list, brand_list)
|
||||
train_fea = features[features.week <= bs.TRAIN_END_WEEK_LIST[r]].reset_index(drop=True)
|
||||
|
||||
# Drop rows with NaN values
|
||||
train_fea.dropna(inplace=True)
|
||||
|
||||
# Create training set
|
||||
dtrain = lgb.Dataset(train_fea.drop("move", axis=1, inplace=False), label=train_fea["move"])
|
||||
if r % 3 == 0:
|
||||
# Train GBM model
|
||||
print("Training model...")
|
||||
bst = lgb.train(params, dtrain, valid_sets=[dtrain], categorical_feature=categ_fea, verbose_eval=False)
|
||||
|
||||
# Generate forecasts
|
||||
print("Making predictions...")
|
||||
test_fea = features[features.week >= bs.TEST_START_WEEK_LIST[r]].reset_index(drop=True)
|
||||
pred = make_predictions(test_fea, bst).sort_values(by=["store", "brand", "week"]).reset_index(drop=True)
|
||||
# Additional columns required by the submission format
|
||||
pred["round"] = r + 1
|
||||
pred["weeks_ahead"] = pred["week"] - bs.TRAIN_END_WEEK_LIST[r]
|
||||
# Keep the predictions
|
||||
pred_all.append(pred)
|
||||
|
||||
# Generate submission
|
||||
submission = pd.concat(pred_all, axis=0)
|
||||
submission.rename(columns={"move": "prediction"}, inplace=True)
|
||||
submission = submission[["round", "store", "brand", "week", "weeks_ahead", "prediction"]]
|
||||
filename = "submission_seed_" + str(args.seed) + ".csv"
|
||||
submission.to_csv(os.path.join(SUBMISSION_DIR, filename), index=False)
|
||||
|
|
@ -0,0 +1,241 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Perform cross validation of a boosted decision tree model on the training data of the 1st forecast round.
|
||||
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import argparse
|
||||
import datetime
|
||||
import itertools
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import lightgbm as lgb
|
||||
from azureml.core import Run
|
||||
from sklearn.model_selection import train_test_split
|
||||
from utils import week_of_month, df_from_cartesian_product
|
||||
|
||||
|
||||
def lagged_features(df, lags):
|
||||
"""Create lagged features based on time series data.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Input time series data sorted by time
|
||||
lags (List): Lag lengths
|
||||
|
||||
Returns:
|
||||
fea (Dataframe): Lagged features
|
||||
"""
|
||||
df_list = []
|
||||
for lag in lags:
|
||||
df_shifted = df.shift(lag)
|
||||
df_shifted.columns = [x + "_lag" + str(lag) for x in df_shifted.columns]
|
||||
df_list.append(df_shifted)
|
||||
fea = pd.concat(df_list, axis=1)
|
||||
return fea
|
||||
|
||||
|
||||
def moving_averages(df, start_step, window_size=None):
|
||||
"""Compute averages of every feature over moving time windows.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Input features as a dataframe
|
||||
start_step (Integer): Starting time step of rolling mean
|
||||
window_size (Integer): Windows size of rolling mean
|
||||
|
||||
Returns:
|
||||
fea (Dataframe): Dataframe consisting of the moving averages
|
||||
"""
|
||||
if window_size == None: # Use a large window to compute average over all historical data
|
||||
window_size = df.shape[0]
|
||||
fea = df.shift(start_step).rolling(min_periods=1, center=False, window=window_size).mean()
|
||||
fea.columns = fea.columns + "_mean"
|
||||
return fea
|
||||
|
||||
|
||||
def combine_features(df, lag_fea, lags, window_size, used_columns):
|
||||
"""Combine different features for a certain store-brand.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Time series data of a certain store-brand
|
||||
lag_fea (List): A list of column names for creating lagged features
|
||||
lags (Numpy Array): Numpy array including all the lags
|
||||
window_size (Integer): Windows size of rolling mean
|
||||
used_columns (List): A list of names of columns used in model training (including target variable)
|
||||
|
||||
Returns:
|
||||
fea_all (Dataframe): Dataframe including all features for the specific store-brand
|
||||
"""
|
||||
lagged_fea = lagged_features(df[lag_fea], lags)
|
||||
moving_avg = moving_averages(df[lag_fea], 2, window_size)
|
||||
fea_all = pd.concat([df[used_columns], lagged_fea, moving_avg], axis=1)
|
||||
return fea_all
|
||||
|
||||
|
||||
def make_predictions(df, model):
|
||||
"""Predict sales with the trained GBM model.
|
||||
|
||||
Args:
|
||||
df (Dataframe): Dataframe including all needed features
|
||||
model (Model): Trained GBM model
|
||||
|
||||
Returns:
|
||||
Dataframe including the predicted sales of a certain store-brand
|
||||
"""
|
||||
predictions = pd.DataFrame({"move": model.predict(df.drop("move", axis=1))})
|
||||
predictions["move"] = predictions["move"].apply(lambda x: round(x))
|
||||
return pd.concat([df[["brand", "store", "week"]].reset_index(drop=True), predictions], axis=1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Parse input arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--data-folder", type=str, dest="data_folder", default=".", help="data folder mounting point")
|
||||
parser.add_argument("--num-leaves", type=int, dest="num_leaves", default=64, help="# of leaves of the tree")
|
||||
parser.add_argument(
|
||||
"--min-data-in-leaf", type=int, dest="min_data_in_leaf", default=50, help="minimum # of samples in each leaf"
|
||||
)
|
||||
parser.add_argument("--learning-rate", type=float, dest="learning_rate", default=0.001, help="learning rate")
|
||||
parser.add_argument(
|
||||
"--feature-fraction",
|
||||
type=float,
|
||||
dest="feature_fraction",
|
||||
default=1.0,
|
||||
help="ratio of features used in each iteration",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--bagging-fraction",
|
||||
type=float,
|
||||
dest="bagging_fraction",
|
||||
default=1.0,
|
||||
help="ratio of samples used in each iteration",
|
||||
)
|
||||
parser.add_argument("--bagging-freq", type=int, dest="bagging_freq", default=1, help="bagging frequency")
|
||||
parser.add_argument("--max-rounds", type=int, dest="max_rounds", default=400, help="# of boosting iterations")
|
||||
parser.add_argument("--max-lag", type=int, dest="max_lag", default=10, help="max lag of unit sales")
|
||||
parser.add_argument(
|
||||
"--window-size", type=int, dest="window_size", default=10, help="window size of moving average of unit sales"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
args.feature_fraction = round(args.feature_fraction, 2)
|
||||
args.bagging_fraction = round(args.bagging_fraction, 2)
|
||||
print(args)
|
||||
|
||||
# Start an Azure ML run
|
||||
run = Run.get_context()
|
||||
|
||||
# Data paths
|
||||
DATA_DIR = args.data_folder
|
||||
TRAIN_DIR = os.path.join(DATA_DIR, "train")
|
||||
|
||||
# Data and forecast problem parameters
|
||||
TRAIN_START_WEEK = 40
|
||||
TRAIN_END_WEEK_LIST = list(range(135, 159, 2))
|
||||
TEST_START_WEEK_LIST = list(range(137, 161, 2))
|
||||
TEST_END_WEEK_LIST = list(range(138, 162, 2))
|
||||
# The start datetime of the first week in the record
|
||||
FIRST_WEEK_START = pd.to_datetime("1989-09-14 00:00:00")
|
||||
|
||||
# Parameters of GBM model
|
||||
params = {
|
||||
"objective": "mape",
|
||||
"num_leaves": args.num_leaves,
|
||||
"min_data_in_leaf": args.min_data_in_leaf,
|
||||
"learning_rate": args.learning_rate,
|
||||
"feature_fraction": args.feature_fraction,
|
||||
"bagging_fraction": args.bagging_fraction,
|
||||
"bagging_freq": args.bagging_freq,
|
||||
"num_rounds": args.max_rounds,
|
||||
"early_stopping_rounds": 125,
|
||||
"num_threads": 16,
|
||||
}
|
||||
|
||||
# Lags and used column names
|
||||
lags = np.arange(2, args.max_lag + 1)
|
||||
used_columns = ["store", "brand", "week", "week_of_month", "month", "deal", "feat", "move", "price", "price_ratio"]
|
||||
categ_fea = ["store", "brand", "deal"]
|
||||
|
||||
# Train and validate the model using only the first round data
|
||||
r = 0
|
||||
print("---- Round " + str(r + 1) + " ----")
|
||||
# Load training data
|
||||
train_df = pd.read_csv(os.path.join(TRAIN_DIR, "train_round_" + str(r + 1) + ".csv"))
|
||||
train_df["move"] = train_df["logmove"].apply(lambda x: round(math.exp(x)))
|
||||
train_df = train_df[["store", "brand", "week", "move"]]
|
||||
|
||||
# Create a dataframe to hold all necessary data
|
||||
store_list = train_df["store"].unique()
|
||||
brand_list = train_df["brand"].unique()
|
||||
week_list = range(TRAIN_START_WEEK, TEST_END_WEEK_LIST[r] + 1)
|
||||
d = {"store": store_list, "brand": brand_list, "week": week_list}
|
||||
data_grid = df_from_cartesian_product(d)
|
||||
data_filled = pd.merge(data_grid, train_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Get future price, deal, and advertisement info
|
||||
aux_df = pd.read_csv(os.path.join(TRAIN_DIR, "aux_round_" + str(r + 1) + ".csv"))
|
||||
data_filled = pd.merge(data_filled, aux_df, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# Create relative price feature
|
||||
price_cols = [
|
||||
"price1",
|
||||
"price2",
|
||||
"price3",
|
||||
"price4",
|
||||
"price5",
|
||||
"price6",
|
||||
"price7",
|
||||
"price8",
|
||||
"price9",
|
||||
"price10",
|
||||
"price11",
|
||||
]
|
||||
data_filled["price"] = data_filled.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
data_filled["avg_price"] = data_filled[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
data_filled["price_ratio"] = data_filled["price"] / data_filled["avg_price"]
|
||||
data_filled.drop(price_cols, axis=1, inplace=True)
|
||||
|
||||
# Fill missing values
|
||||
data_filled = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: x.fillna(method="ffill").fillna(method="bfill")
|
||||
)
|
||||
|
||||
# Create datetime features
|
||||
data_filled["week_start"] = data_filled["week"].apply(
|
||||
lambda x: FIRST_WEEK_START + datetime.timedelta(days=(x - 1) * 7)
|
||||
)
|
||||
data_filled["year"] = data_filled["week_start"].apply(lambda x: x.year)
|
||||
data_filled["month"] = data_filled["week_start"].apply(lambda x: x.month)
|
||||
data_filled["week_of_month"] = data_filled["week_start"].apply(lambda x: week_of_month(x))
|
||||
data_filled["day"] = data_filled["week_start"].apply(lambda x: x.day)
|
||||
data_filled.drop("week_start", axis=1, inplace=True)
|
||||
|
||||
# Create other features (lagged features, moving averages, etc.)
|
||||
features = data_filled.groupby(["store", "brand"]).apply(
|
||||
lambda x: combine_features(x, ["move"], lags, args.window_size, used_columns)
|
||||
)
|
||||
train_fea = features[features.week <= TRAIN_END_WEEK_LIST[r]].reset_index(drop=True)
|
||||
|
||||
# Drop rows with NaN values
|
||||
train_fea.dropna(inplace=True)
|
||||
|
||||
# Model training and validation
|
||||
# Create a training/validation split
|
||||
train_fea, valid_fea, train_label, valid_label = train_test_split(
|
||||
train_fea.drop("move", axis=1, inplace=False), train_fea["move"], test_size=0.05, random_state=1
|
||||
)
|
||||
dtrain = lgb.Dataset(train_fea, train_label)
|
||||
dvalid = lgb.Dataset(valid_fea, valid_label)
|
||||
# A dictionary to record training results
|
||||
evals_result = {}
|
||||
# Train GBM model
|
||||
bst = lgb.train(
|
||||
params, dtrain, valid_sets=[dtrain, dvalid], categorical_feature=categ_fea, evals_result=evals_result
|
||||
)
|
||||
# Get final training loss & validation loss
|
||||
train_loss = evals_result["training"]["mape"][-1]
|
||||
valid_loss = evals_result["valid_1"]["mape"][-1]
|
||||
print("Final training loss is {}".format(train_loss))
|
||||
print("Final validation loss is {}".format(valid_loss))
|
||||
|
||||
# Log the validation loss/MAPE
|
||||
run.log("MAPE", np.float(valid_loss) * 100)
|
||||
|
|
@ -1,9 +1,10 @@
|
|||
# coding: utf-8
|
||||
|
||||
# Utility functions for building the boosted decision tree model.
|
||||
# Utility functions for building the boosted decision tree model.
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def week_of_month(dt):
|
||||
"""Get the week of the month for the specified date.
|
||||
|
||||
|
|
@ -12,15 +13,17 @@ def week_of_month(dt):
|
|||
|
||||
Returns:
|
||||
wom (Integer): Week of the month of the input date
|
||||
"""
|
||||
"""
|
||||
from math import ceil
|
||||
|
||||
first_day = dt.replace(day=1)
|
||||
dom = dt.day
|
||||
adjusted_dom = dom + first_day.weekday()
|
||||
wom = int(ceil(adjusted_dom/7.0))
|
||||
wom = int(ceil(adjusted_dom / 7.0))
|
||||
return wom
|
||||
|
||||
def df_from_cartesian_product(dict_in):
|
||||
|
||||
def df_from_cartesian_product(dict_in):
|
||||
"""Generate a Pandas dataframe from Cartesian product of lists.
|
||||
|
||||
Args:
|
||||
|
|
@ -31,7 +34,8 @@ def df_from_cartesian_product(dict_in):
|
|||
"""
|
||||
from collections import OrderedDict
|
||||
from itertools import product
|
||||
|
||||
od = OrderedDict(sorted(dict_in.items()))
|
||||
cart = list(product(*od.values()))
|
||||
df = pd.DataFrame(cart, columns=od.keys())
|
||||
return df
|
||||
return df
|
||||
|
|
@ -22,7 +22,7 @@ hparams_manual = dict(
|
|||
learning_rate=0.001,
|
||||
beta1=0.9,
|
||||
beta2=0.999,
|
||||
epsilon=1e-08
|
||||
epsilon=1e-08,
|
||||
)
|
||||
# this is the hyperparameter selected when running 50 trials in SMAC
|
||||
# hyperparameter tuning.
|
||||
|
|
@ -43,7 +43,7 @@ hparams_smac = dict(
|
|||
learning_rate=0.001,
|
||||
beta1=0.7763754022206656,
|
||||
beta2=0.7923825287287111,
|
||||
epsilon=1e-08
|
||||
epsilon=1e-08,
|
||||
)
|
||||
|
||||
# this is the hyperparameter selected when running 100 trials in SMAC
|
||||
|
|
@ -68,5 +68,5 @@ hparams_smac_100 = dict(
|
|||
learning_rate=0.01,
|
||||
beta1=0.6011027681578323,
|
||||
beta2=0.9809964662293627,
|
||||
epsilon=1e-08
|
||||
)
|
||||
epsilon=1e-08,
|
||||
)
|
||||
|
|
@ -22,7 +22,7 @@ from utils import *
|
|||
|
||||
# Add TSPerf root directory to sys.path
|
||||
file_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
|
||||
tsperf_dir = os.path.join(file_dir, '../../../../')
|
||||
tsperf_dir = os.path.join(file_dir, "../../../../")
|
||||
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
|
@ -32,13 +32,16 @@ from common.evaluation_utils import MAPE
|
|||
|
||||
from smac.configspace import ConfigurationSpace
|
||||
from smac.scenario.scenario import Scenario
|
||||
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
|
||||
UniformFloatHyperparameter, UniformIntegerHyperparameter
|
||||
from ConfigSpace.hyperparameters import (
|
||||
CategoricalHyperparameter,
|
||||
UniformFloatHyperparameter,
|
||||
UniformIntegerHyperparameter,
|
||||
)
|
||||
from smac.facade.smac_facade import SMAC
|
||||
|
||||
|
||||
LIST_HYPERPARAMETER = ['decoder_input_dropout', 'decoder_state_dropout', 'decoder_output_dropout']
|
||||
data_relative_dir = '../../data'
|
||||
LIST_HYPERPARAMETER = ["decoder_input_dropout", "decoder_state_dropout", "decoder_output_dropout"]
|
||||
data_relative_dir = "../../data"
|
||||
|
||||
|
||||
def eval_function(hparams_dict):
|
||||
|
|
@ -56,10 +59,10 @@ def eval_function(hparams_dict):
|
|||
hparams_dict[key] = [hparams_dict[key]]
|
||||
|
||||
# add the value of other hyper parameters which are not tuned
|
||||
hparams_dict['encoder_rnn_layers'] = 1
|
||||
hparams_dict['decoder_rnn_layers'] = 1
|
||||
hparams_dict['decoder_variational_dropout'] = [False]
|
||||
hparams_dict['asgd_decay'] = None
|
||||
hparams_dict["encoder_rnn_layers"] = 1
|
||||
hparams_dict["decoder_rnn_layers"] = 1
|
||||
hparams_dict["decoder_variational_dropout"] = [False]
|
||||
hparams_dict["asgd_decay"] = None
|
||||
|
||||
hparams = training.HParams(**hparams_dict)
|
||||
# use round 1 training data for hyper parameter tuning to avoid data leakage for later rounds
|
||||
|
|
@ -67,106 +70,113 @@ def eval_function(hparams_dict):
|
|||
make_features_flag = False
|
||||
train_model_flag = True
|
||||
train_back_offset = 3 # equal to predict_window
|
||||
predict_cut_mode = 'eval'
|
||||
predict_cut_mode = "eval"
|
||||
|
||||
# get prediction
|
||||
pred_o, train_mape = create_round_prediction(data_dir, submission_round, hparams, make_features_flag=make_features_flag,
|
||||
train_model_flag=train_model_flag, train_back_offset=train_back_offset,
|
||||
predict_cut_mode=predict_cut_mode)
|
||||
pred_o, train_mape = create_round_prediction(
|
||||
data_dir,
|
||||
submission_round,
|
||||
hparams,
|
||||
make_features_flag=make_features_flag,
|
||||
train_model_flag=train_model_flag,
|
||||
train_back_offset=train_back_offset,
|
||||
predict_cut_mode=predict_cut_mode,
|
||||
)
|
||||
# get rid of prediction at horizon 1
|
||||
pred_sub = pred_o[:, 1:].reshape((-1))
|
||||
|
||||
# evaluate the prediction on last two days in the first round training data
|
||||
# TODO: get train error and evalution error for different parameters
|
||||
train_file = os.path.join(data_dir, 'train/train_round_{}.csv'.format(submission_round))
|
||||
train_file = os.path.join(data_dir, "train/train_round_{}.csv".format(submission_round))
|
||||
train = pd.read_csv(train_file, index_col=False)
|
||||
train_last_week = bs.TRAIN_END_WEEK_LIST[submission_round - 1]
|
||||
# filter the train to contain ony last two days' data
|
||||
train = train.loc[train['week'] >= train_last_week - 1]
|
||||
train = train.loc[train["week"] >= train_last_week - 1]
|
||||
|
||||
# create the data frame without missing dates
|
||||
store_list = train['store'].unique()
|
||||
brand_list = train['brand'].unique()
|
||||
store_list = train["store"].unique()
|
||||
brand_list = train["brand"].unique()
|
||||
week_list = range(train_last_week - 1, train_last_week + 1)
|
||||
item_list = list(itertools.product(store_list, brand_list, week_list))
|
||||
item_df = pd.DataFrame.from_records(item_list, columns=['store', 'brand', 'week'])
|
||||
item_df = pd.DataFrame.from_records(item_list, columns=["store", "brand", "week"])
|
||||
|
||||
train = item_df.merge(train, how='left', on=['store', 'brand', 'week'])
|
||||
result = train.sort_values(by=['store', 'brand', 'week'], ascending=True)
|
||||
result['prediction'] = pred_sub
|
||||
result['sales'] = result['logmove'].apply(lambda x: round(np.exp(x)))
|
||||
train = item_df.merge(train, how="left", on=["store", "brand", "week"])
|
||||
result = train.sort_values(by=["store", "brand", "week"], ascending=True)
|
||||
result["prediction"] = pred_sub
|
||||
result["sales"] = result["logmove"].apply(lambda x: round(np.exp(x)))
|
||||
|
||||
# calculate MAPE on the evaluate set
|
||||
result = result.loc[result['sales'].notnull()]
|
||||
eval_mape = MAPE(result['prediction'], result['sales'])
|
||||
result = result.loc[result["sales"].notnull()]
|
||||
eval_mape = MAPE(result["prediction"], result["sales"])
|
||||
return eval_mape
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
|
||||
# Build Configuration Space which defines all parameters and their ranges
|
||||
cs = ConfigurationSpace()
|
||||
|
||||
# add parameters to the configuration space
|
||||
train_window = UniformIntegerHyperparameter('train_window', 3, 70, default_value=60)
|
||||
train_window = UniformIntegerHyperparameter("train_window", 3, 70, default_value=60)
|
||||
cs.add_hyperparameter(train_window)
|
||||
|
||||
batch_size = CategoricalHyperparameter('batch_size', [64, 128, 256, 1024], default_value=64)
|
||||
batch_size = CategoricalHyperparameter("batch_size", [64, 128, 256, 1024], default_value=64)
|
||||
cs.add_hyperparameter(batch_size)
|
||||
|
||||
rnn_depth = UniformIntegerHyperparameter('rnn_depth', 100, 500, default_value=400)
|
||||
rnn_depth = UniformIntegerHyperparameter("rnn_depth", 100, 500, default_value=400)
|
||||
cs.add_hyperparameter(rnn_depth)
|
||||
|
||||
encoder_dropout = UniformFloatHyperparameter('encoder_dropout', 0.0, 0.05, default_value=0.03)
|
||||
encoder_dropout = UniformFloatHyperparameter("encoder_dropout", 0.0, 0.05, default_value=0.03)
|
||||
cs.add_hyperparameter(encoder_dropout)
|
||||
|
||||
gate_dropout = UniformFloatHyperparameter('gate_dropout', 0.95, 1.0, default_value=0.997)
|
||||
gate_dropout = UniformFloatHyperparameter("gate_dropout", 0.95, 1.0, default_value=0.997)
|
||||
cs.add_hyperparameter(gate_dropout)
|
||||
|
||||
decoder_input_dropout = UniformFloatHyperparameter('decoder_input_dropout', 0.95, 1.0, default_value=1.0)
|
||||
decoder_input_dropout = UniformFloatHyperparameter("decoder_input_dropout", 0.95, 1.0, default_value=1.0)
|
||||
cs.add_hyperparameter(decoder_input_dropout)
|
||||
|
||||
decoder_state_dropout = UniformFloatHyperparameter('decoder_state_dropout', 0.95, 1.0, default_value=0.99)
|
||||
decoder_state_dropout = UniformFloatHyperparameter("decoder_state_dropout", 0.95, 1.0, default_value=0.99)
|
||||
cs.add_hyperparameter(decoder_state_dropout)
|
||||
|
||||
decoder_output_dropout = UniformFloatHyperparameter('decoder_output_dropout', 0.95, 1.0, default_value=0.975)
|
||||
decoder_output_dropout = UniformFloatHyperparameter("decoder_output_dropout", 0.95, 1.0, default_value=0.975)
|
||||
cs.add_hyperparameter(decoder_output_dropout)
|
||||
|
||||
max_epoch = CategoricalHyperparameter('max_epoch', [50, 100, 150, 200], default_value=100)
|
||||
max_epoch = CategoricalHyperparameter("max_epoch", [50, 100, 150, 200], default_value=100)
|
||||
cs.add_hyperparameter(max_epoch)
|
||||
|
||||
learning_rate = CategoricalHyperparameter('learning_rate', [0.001, 0.01, 0.1], default_value=0.001)
|
||||
learning_rate = CategoricalHyperparameter("learning_rate", [0.001, 0.01, 0.1], default_value=0.001)
|
||||
cs.add_hyperparameter(learning_rate)
|
||||
|
||||
beta1 = UniformFloatHyperparameter('beta1', 0.5, 0.9999, default_value=0.9)
|
||||
beta1 = UniformFloatHyperparameter("beta1", 0.5, 0.9999, default_value=0.9)
|
||||
cs.add_hyperparameter(beta1)
|
||||
|
||||
beta2 = UniformFloatHyperparameter('beta2', 0.5, 0.9999, default_value=0.999)
|
||||
beta2 = UniformFloatHyperparameter("beta2", 0.5, 0.9999, default_value=0.999)
|
||||
cs.add_hyperparameter(beta2)
|
||||
|
||||
epsilon = CategoricalHyperparameter('epsilon', [1e-08, 0.00001, 0.0001, 0.1, 1], default_value=1e-08)
|
||||
epsilon = CategoricalHyperparameter("epsilon", [1e-08, 0.00001, 0.0001, 0.1, 1], default_value=1e-08)
|
||||
cs.add_hyperparameter(epsilon)
|
||||
|
||||
scenario = Scenario({"run_obj": "quality", # we optimize quality (alternatively runtime)
|
||||
"runcount-limit": 50, # maximum function evaluations
|
||||
"cs": cs, # configuration space
|
||||
"deterministic": "true"
|
||||
})
|
||||
scenario = Scenario(
|
||||
{
|
||||
"run_obj": "quality", # we optimize quality (alternatively runtime)
|
||||
"runcount-limit": 50, # maximum function evaluations
|
||||
"cs": cs, # configuration space
|
||||
"deterministic": "true",
|
||||
}
|
||||
)
|
||||
|
||||
# test the default configuration works
|
||||
# eval_function(cs.get_default_configuration())
|
||||
|
||||
|
||||
# import hyper parameters
|
||||
# TODO: add ema in the code to imporve the performance
|
||||
smac = SMAC(scenario=scenario, rng=np.random.RandomState(42),
|
||||
tae_runner=eval_function)
|
||||
smac = SMAC(scenario=scenario, rng=np.random.RandomState(42), tae_runner=eval_function)
|
||||
|
||||
incumbent = smac.optimize()
|
||||
inc_value = eval_function(incumbent)
|
||||
print('the best hyper parameter sets are:')
|
||||
print("the best hyper parameter sets are:")
|
||||
print(incumbent)
|
||||
print('the corresponding MAPE on validation datset is: {}'.format(inc_value))
|
||||
print("the corresponding MAPE on validation datset is: {}".format(inc_value))
|
||||
|
||||
# following are the print out:
|
||||
# the best hyper parameter sets are:
|
||||
|
|
@ -186,6 +196,3 @@ if __name__ == '__main__':
|
|||
# train_window, Value: 26
|
||||
|
||||
# the corresponding MAPE is: 0.36703585613035433
|
||||
|
||||
|
||||
|
||||
|
|
@ -14,14 +14,14 @@ from sklearn.preprocessing import OneHotEncoder
|
|||
|
||||
# Add TSPerf root directory to sys.path
|
||||
file_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
|
||||
tsperf_dir = os.path.join(file_dir, '../../../../')
|
||||
tsperf_dir = os.path.join(file_dir, "../../../../")
|
||||
|
||||
if tsperf_dir not in sys.path:
|
||||
sys.path.append(tsperf_dir)
|
||||
|
||||
import retail_sales.OrangeJuice_Pt_3Weeks_Weekly.common.benchmark_settings as bs
|
||||
|
||||
data_relative_dir = '../../data'
|
||||
data_relative_dir = "../../data"
|
||||
|
||||
|
||||
def make_features(submission_round):
|
||||
|
|
@ -37,63 +37,77 @@ def make_features(submission_round):
|
|||
file_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
|
||||
data_dir = os.path.join(file_dir, data_relative_dir)
|
||||
|
||||
train_file = os.path.join(data_dir, 'train/train_round_{}.csv'.format(submission_round))
|
||||
test_file = os.path.join(data_dir, 'train/aux_round_{}.csv'.format(submission_round))
|
||||
train_file = os.path.join(data_dir, "train/train_round_{}.csv".format(submission_round))
|
||||
test_file = os.path.join(data_dir, "train/aux_round_{}.csv".format(submission_round))
|
||||
|
||||
train = pd.read_csv(train_file, index_col=False)
|
||||
test = pd.read_csv(test_file, index_col=False)
|
||||
|
||||
# select the test data range for test data
|
||||
train_last_week = train['week'].max()
|
||||
test = test.loc[test['week'] > train_last_week]
|
||||
train_last_week = train["week"].max()
|
||||
test = test.loc[test["week"] > train_last_week]
|
||||
|
||||
# calculate series popularity
|
||||
series_popularity = train.groupby(['store', 'brand']).apply(lambda x: x['logmove'].median())
|
||||
series_popularity = train.groupby(["store", "brand"]).apply(lambda x: x["logmove"].median())
|
||||
|
||||
# fill the datetime gaps
|
||||
# such that every time series have the same length both in train and test
|
||||
store_list = train['store'].unique()
|
||||
brand_list = train['brand'].unique()
|
||||
store_list = train["store"].unique()
|
||||
brand_list = train["brand"].unique()
|
||||
train_week_list = range(bs.TRAIN_START_WEEK, bs.TRAIN_END_WEEK_LIST[submission_round - 1] + 1)
|
||||
test_week_list = range(bs.TEST_START_WEEK_LIST[submission_round - 1] - 1, bs.TEST_END_WEEK_LIST[submission_round - 1] + 1)
|
||||
test_week_list = range(
|
||||
bs.TEST_START_WEEK_LIST[submission_round - 1] - 1, bs.TEST_END_WEEK_LIST[submission_round - 1] + 1
|
||||
)
|
||||
|
||||
train_item_list = list(itertools.product(store_list, brand_list, train_week_list))
|
||||
train_item_df = pd.DataFrame.from_records(train_item_list, columns=['store', 'brand', 'week'])
|
||||
train_item_df = pd.DataFrame.from_records(train_item_list, columns=["store", "brand", "week"])
|
||||
|
||||
test_item_list = list(itertools.product(store_list, brand_list, test_week_list))
|
||||
test_item_df = pd.DataFrame.from_records(test_item_list, columns=['store', 'brand', 'week'])
|
||||
test_item_df = pd.DataFrame.from_records(test_item_list, columns=["store", "brand", "week"])
|
||||
|
||||
train = train_item_df.merge(train, how='left', on=['store', 'brand', 'week'])
|
||||
test = test_item_df.merge(test, how='left', on=['store', 'brand', 'week'])
|
||||
train = train_item_df.merge(train, how="left", on=["store", "brand", "week"])
|
||||
test = test_item_df.merge(test, how="left", on=["store", "brand", "week"])
|
||||
|
||||
# sort the train, test, series_popularity by store and brand
|
||||
train = train.sort_values(by=['store', 'brand', 'week'], ascending=True)
|
||||
test = test.sort_values(by=['store', 'brand', 'week'], ascending=True)
|
||||
series_popularity = series_popularity.reset_index().sort_values(
|
||||
by=['store', 'brand'], ascending=True).rename(columns={0: 'pop'})
|
||||
train = train.sort_values(by=["store", "brand", "week"], ascending=True)
|
||||
test = test.sort_values(by=["store", "brand", "week"], ascending=True)
|
||||
series_popularity = (
|
||||
series_popularity.reset_index().sort_values(by=["store", "brand"], ascending=True).rename(columns={0: "pop"})
|
||||
)
|
||||
|
||||
# calculate one-hot encoding for brands
|
||||
enc = OneHotEncoder(categories='auto')
|
||||
brand_train = np.reshape(train['brand'].values, (-1, 1))
|
||||
brand_test = np.reshape(test['brand'].values, (-1, 1))
|
||||
enc = OneHotEncoder(categories="auto")
|
||||
brand_train = np.reshape(train["brand"].values, (-1, 1))
|
||||
brand_test = np.reshape(test["brand"].values, (-1, 1))
|
||||
enc = enc.fit(brand_train)
|
||||
brand_enc_train = enc.transform(brand_train).todense()
|
||||
brand_enc_test = enc.transform(brand_test).todense()
|
||||
|
||||
# calculate price and price_ratio
|
||||
price_cols = ['price1', 'price2', 'price3', 'price4', 'price5', 'price6', 'price7', 'price8',
|
||||
'price9', 'price10', 'price11']
|
||||
price_cols = [
|
||||
"price1",
|
||||
"price2",
|
||||
"price3",
|
||||
"price4",
|
||||
"price5",
|
||||
"price6",
|
||||
"price7",
|
||||
"price8",
|
||||
"price9",
|
||||
"price10",
|
||||
"price11",
|
||||
]
|
||||
|
||||
train['price'] = train.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
|
||||
train['avg_price'] = train[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
train['price_ratio'] = train['price'] / train['avg_price']
|
||||
train["price"] = train.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
train["avg_price"] = train[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
train["price_ratio"] = train["price"] / train["avg_price"]
|
||||
|
||||
test['price'] = test.apply(lambda x: x.loc['price' + str(int(x.loc['brand']))], axis=1)
|
||||
test['avg_price'] = test[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
test['price_ratio'] = test.apply(lambda x: x['price'] / x['avg_price'], axis=1)
|
||||
test["price"] = test.apply(lambda x: x.loc["price" + str(int(x.loc["brand"]))], axis=1)
|
||||
test["avg_price"] = test[price_cols].sum(axis=1).apply(lambda x: x / len(price_cols))
|
||||
test["price_ratio"] = test.apply(lambda x: x["price"] / x["avg_price"], axis=1)
|
||||
|
||||
# fill the missing values for feat, deal, price, price_ratio with 0
|
||||
for cl in ['price', 'price_ratio', 'feat', 'deal']:
|
||||
for cl in ["price", "price_ratio", "feat", "deal"]:
|
||||
train.loc[train[cl].isna(), cl] = 0
|
||||
test.loc[test[cl].isna(), cl] = 0
|
||||
|
||||
|
|
@ -102,16 +116,15 @@ def make_features(submission_round):
|
|||
# 2) brand - 11
|
||||
# 3) price: price and price_ratio
|
||||
# 4) promo: feat and deal
|
||||
series_popularity = series_popularity['pop'].values
|
||||
series_popularity = (series_popularity - series_popularity.mean()) \
|
||||
/ np.std(series_popularity)
|
||||
series_popularity = series_popularity["pop"].values
|
||||
series_popularity = (series_popularity - series_popularity.mean()) / np.std(series_popularity)
|
||||
|
||||
brand_enc_mean = brand_enc_train.mean(axis=0)
|
||||
brand_enc_std = brand_enc_train.std(axis=0)
|
||||
brand_enc_train = (brand_enc_train - brand_enc_mean) / brand_enc_std
|
||||
brand_enc_test = (brand_enc_test - brand_enc_mean) / brand_enc_std
|
||||
|
||||
for cl in ['price', 'price_ratio', 'feat', 'deal']:
|
||||
for cl in ["price", "price_ratio", "feat", "deal"]:
|
||||
cl_mean = train[cl].mean()
|
||||
cl_std = train[cl].std()
|
||||
train[cl] = (train[cl] - cl_mean) / cl_std
|
||||
|
|
@ -132,41 +145,37 @@ def make_features(submission_round):
|
|||
|
||||
# ts_value_train
|
||||
# the target variable fed into the neural network are: log(sales + 1), where sales = exp(logmove).
|
||||
ts_value_train = np.log(np.exp(train['logmove'].values) + 1)
|
||||
ts_value_train = np.log(np.exp(train["logmove"].values) + 1)
|
||||
ts_value_train = ts_value_train.reshape((ts_number, train_ts_length))
|
||||
# fill missing value with zero
|
||||
ts_value_train = np.nan_to_num(ts_value_train)
|
||||
|
||||
# feature_train
|
||||
series_popularity_train = np.repeat(series_popularity, train_ts_length).reshape(
|
||||
(ts_number, train_ts_length, 1))
|
||||
series_popularity_train = np.repeat(series_popularity, train_ts_length).reshape((ts_number, train_ts_length, 1))
|
||||
|
||||
brand_number = brand_enc_train.shape[1]
|
||||
brand_enc_train = np.array(brand_enc_train).reshape(
|
||||
(ts_number, train_ts_length, brand_number))
|
||||
price_promo_features_train = train[['price', 'price_ratio', 'feat', 'deal']].values.reshape(
|
||||
(ts_number, train_ts_length, 4))
|
||||
brand_enc_train = np.array(brand_enc_train).reshape((ts_number, train_ts_length, brand_number))
|
||||
price_promo_features_train = train[["price", "price_ratio", "feat", "deal"]].values.reshape(
|
||||
(ts_number, train_ts_length, 4)
|
||||
)
|
||||
|
||||
feature_train = np.concatenate((series_popularity_train, brand_enc_train, price_promo_features_train), axis=-1)
|
||||
|
||||
# feature_test
|
||||
series_popularity_test = np.repeat(series_popularity, test_ts_length).reshape(
|
||||
(ts_number, test_ts_length, 1))
|
||||
series_popularity_test = np.repeat(series_popularity, test_ts_length).reshape((ts_number, test_ts_length, 1))
|
||||
|
||||
brand_enc_test = np.array(brand_enc_test).reshape(
|
||||
(ts_number, test_ts_length, brand_number))
|
||||
price_promo_features_test = test[['price', 'price_ratio', 'feat', 'deal']].values.reshape(
|
||||
(ts_number, test_ts_length, 4))
|
||||
brand_enc_test = np.array(brand_enc_test).reshape((ts_number, test_ts_length, brand_number))
|
||||
price_promo_features_test = test[["price", "price_ratio", "feat", "deal"]].values.reshape(
|
||||
(ts_number, test_ts_length, 4)
|
||||
)
|
||||
|
||||
feature_test = np.concatenate((series_popularity_test, brand_enc_test, price_promo_features_test), axis=-1)
|
||||
|
||||
# save the numpy arrays
|
||||
intermediate_data_dir = os.path.join(data_dir, 'intermediate/round_{}'.format(submission_round))
|
||||
intermediate_data_dir = os.path.join(data_dir, "intermediate/round_{}".format(submission_round))
|
||||
if not os.path.isdir(intermediate_data_dir):
|
||||
os.makedirs(intermediate_data_dir)
|
||||
|
||||
np.save(os.path.join(intermediate_data_dir, 'ts_value_train.npy'), ts_value_train)
|
||||
np.save(os.path.join(intermediate_data_dir, 'feature_train.npy'), feature_train)
|
||||
np.save(os.path.join(intermediate_data_dir, 'feature_test.npy'), feature_test)
|
||||
|
||||
|
||||
np.save(os.path.join(intermediate_data_dir, "ts_value_train.npy"), ts_value_train)
|
||||
np.save(os.path.join(intermediate_data_dir, "feature_train.npy"), feature_train)
|
||||
np.save(os.path.join(intermediate_data_dir, "feature_test.npy"), feature_test)
|
||||
|
|
@ -13,8 +13,17 @@ from utils import *
|
|||
IS_TRAIN = False
|
||||
|
||||
|
||||
def rnn_predict(ts_value_train, feature_train, feature_test, hparams, predict_window, intermediate_data_dir,
|
||||
submission_round, batch_size, cut_mode='predict'):
|
||||
def rnn_predict(
|
||||
ts_value_train,
|
||||
feature_train,
|
||||
feature_test,
|
||||
hparams,
|
||||
predict_window,
|
||||
intermediate_data_dir,
|
||||
submission_round,
|
||||
batch_size,
|
||||
cut_mode="predict",
|
||||
):
|
||||
"""
|
||||
This function creates predictions by loading the trained RNN model.
|
||||
|
||||
|
|
@ -39,13 +48,21 @@ def rnn_predict(ts_value_train, feature_train, feature_test, hparams, predict_wi
|
|||
(#time series, #predict_window)
|
||||
"""
|
||||
# build the dataset
|
||||
root_ds = tf.data.Dataset.from_tensor_slices(
|
||||
(ts_value_train, feature_train, feature_test)).repeat(1)
|
||||
batch = (root_ds
|
||||
.map(lambda *x: cut(*x, cut_mode=cut_mode, train_window=hparams.train_window,
|
||||
predict_window=predict_window, ts_length=ts_value_train.shape[1], back_offset=0))
|
||||
.map(normalize_target)
|
||||
.batch(batch_size))
|
||||
root_ds = tf.data.Dataset.from_tensor_slices((ts_value_train, feature_train, feature_test)).repeat(1)
|
||||
batch = (
|
||||
root_ds.map(
|
||||
lambda *x: cut(
|
||||
*x,
|
||||
cut_mode=cut_mode,
|
||||
train_window=hparams.train_window,
|
||||
predict_window=predict_window,
|
||||
ts_length=ts_value_train.shape[1],
|
||||
back_offset=0
|
||||
)
|
||||
)
|
||||
.map(normalize_target)
|
||||
.batch(batch_size)
|
||||
)
|
||||
|
||||
iterator = batch.make_initializable_iterator()
|
||||
it_tensors = iterator.get_next()
|
||||
|
|
@ -55,9 +72,9 @@ def rnn_predict(ts_value_train, feature_train, feature_test, hparams, predict_wi
|
|||
predictions = build_rnn_model(norm_x, feature_x, feature_y, norm_mean, norm_std, predict_window, IS_TRAIN, hparams)
|
||||
|
||||
# init the saver
|
||||
saver = tf.train.Saver(name='eval_saver', var_list=None)
|
||||
saver = tf.train.Saver(name="eval_saver", var_list=None)
|
||||
# read the saver from checkpoint
|
||||
saver_path = os.path.join(intermediate_data_dir, 'cpt_round_{}'.format(submission_round))
|
||||
saver_path = os.path.join(intermediate_data_dir, "cpt_round_{}".format(submission_round))
|
||||
paths = [p for p in tf.train.get_checkpoint_state(saver_path).all_model_checkpoint_paths]
|
||||
checkpoint = paths[0]
|
||||
|
||||
|
|
@ -66,12 +83,10 @@ def rnn_predict(ts_value_train, feature_train, feature_test, hparams, predict_wi
|
|||
sess.run(iterator.initializer)
|
||||
saver.restore(sess, checkpoint)
|
||||
|
||||
pred, = sess.run([predictions])
|
||||
(pred,) = sess.run([predictions])
|
||||
|
||||
# invert the prediction back to original scale
|
||||
pred_o = np.exp(pred) - 1
|
||||
pred_o = pred_o.astype(int)
|
||||
|
||||
return pred_o
|
||||
|
||||
|
||||
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