brandon-lau0/ECS-171-Course-Project

ECS-171-Course-Project

Introduction

The code is entered through main.py. When run, it will create a DataSet_Builder object which essentially does the preprocessing on the data and can build a DataSet object which runs the machine learning methods. Results are saved to a json file for later viewing.

Runing the Code

Run the code using

python3 main.py <dataset-level_params>.json <grid_search_params>.json

• For example, one could run:

  python3 main.py singleparam.json singleannparam.json

• To get the dataset-level param results, we used:

  python3 main.py params1.json singleannparam.json

• To get the grid search results, use

  python3 main.py params.json ann_params.json

  Note that in order to break up the results, we actually ran several different files called ann_params_*.json

These json files are constructed using parambuild.py (more notes below) and contain a list of dictionaries. The dictionaries contain the parameter values used to construct the DataSet and run the models.

Relevant Files

Here's a list of current files/folders that are relevant.

    .
    +-- dataset.py
    +-- datasetbuilder.py
    +-- main.py
    +-- *.json
    +-- gridsearch.ps1
    +-- scrape.py
    +-- parambuild.py
    +-- OLS.py
    +-- ANN.py
    +-- TSNN.py
    +-- _data
    |   +-- site*.pkl
    |   +-- (data.pkl)
    |   +-- info.txt
    |   +-- merged.csv
    |   +-- sitedict.py
    +-- _results
    |   +-- _dataset-level_params
    |   +-- _grid_searches

We will not cover a full list of attributes here, but more information about each of the code files can be found in the files.

• dataset.py contains the DataSet class with these and other attributes:
  • impute_inputs(): takes in a future date and makes an estimation of the "X" input matrix values for that date by averaging values from that day and surrounding days from previous years
  • run_OLS(): runs the OLS functions and stores results
  • run_ANN(): runs the ANN functions and stores results
  • run_TSNN(): runs the TSNN functions and stores results
• datasetbuilder.py: contains the DataSet_Builder class with these and other attributes:
  • clean_df(): drops rows with NaN or -99.9 values
  • format_date(): converts to cylindrical representation of dates
  • use_rect_radius(): reduces number of sites by rectangular radius
  • use_pca(): uses PCA to reduce number of features
  • remove_outliers(): uses IsolationForest to remove outliers
  • scale_data(): min-max scales data from 0 to 1
  • build_dataset(): builds a DataSet object
• main.py is the main function to run, uses a DataSet_Builder object to build a DataSet object, runs ML methods, and save results
• *.json: several files used as inputs to main.py
• gridsearch.ps1: the final code was run on Windows, so this is a Windows PowerShell script that basically just runs main.py with different command line arguments
• scrape.py is a standalone script that scrapes the website for data
  • gets each site from SITEDICT
  • each year from 1980 to 2019
• parambuild.py is a standalone script that uses lists and for loops to build a list of dictionaries of parameter combinations, both for dataset-level parameters and for neural net parameters used by the grid search. The json files it saves can be used as command line arguments for main.py.
• OLS.py contains functions that compute the OLS and make a prediction
• ANN.py contains functions that create a FFNN model, train it, and make a prediction
• TSNN.py contains functions that train a time series recurrent neural net and make predictions for the next four weeks.
• data folder
  • site*.pkl: these are individual pickles for each site (all years)
  • data.pkl: overall data pickle
    • only appears after constructing DataSet object
    • is over 100 MB, so it's in the gitignore
  • info.txt: site info, including, number, name, latitude, longitude
  • merged.csv: csv file of all data
    • currently ordered by date then site
    • note that the data is rather sparse
    • if you make changes, delete data.pkl (which will regenerate)
  • sitedict.py: uses info.txt to build a dictionary SITEDICT of sites
• results folder
  • dataset-level_params folder: contains graphs and a json file of results for various combinations of dataset-level parameters, used to determine the optimal dataset-level parameters. The folder also contains an INFO.txt file with more information
  • grid_search_params folder: contains multiple subfolders with graphs and result json files that make up the grid search. The folder contains an INFO.txt file that has more information.