Visualizing Interpolation using GridDS

• Following from the previous example interpolation experiment we now demonstrate how to use gridds visualization tools to inspect the results of forecasted data.

• Here we use synthetic data from Smart-DS and downloaded from BetterGrids.org

import sys
from gridds.experimenter import Experimenter
from gridds.data import SmartDS
import gridds.viz.viz as viz_tools
import os
import matplotlib.pyplot as plt
from gridds.models import KNN, RandomForest, BayesianRidge
import gridds.tools.tasks as tasks
import shutil
import pandas as pd
import pickle

Recap running an interpolation experiment

• Here is a brief recap on how to run the interpolation experiment with inline comments provided.

# run experiments from root dir ( one up)
os.chdir('../')

# instantiate smartDS dataset
dataset = SmartDS('univariate_nrel', sites=1, test_pct=.5, normalize=False, size=300)

# load customer data
reader_instructions = {
    'sources': ['P1U'],
    'modalities': ['load_data'],
    'target': '',  # NREL synthetic data doesn't have faults
    'replicates': ['customers']
}

# prepare x/y split of training data
dataset.prepare_data(reader_instructions)

#ablate data so we can test interpolation
dataset.remove_data(chunksize=20, chunks=3)

# compile list of methods to use
methods = []
methods += [KNN('KNN')]
methods += [ BayesianRidge('Bayesian Ridge')]
methods += [RandomForest('Random Forest')]

# run methods on task : "tasks.default_autoregression"
exp = Experimenter('interpolation_viz', runs=1)
exp.run_experiment(dataset,methods,task=tasks.default_autoregression, clean=False)

      method_name       mae     rmse
0             KNN  0.252573  0.46692
1  Bayesian Ridge  0.252573  0.46692
2   Random Forest  0.252573  0.46692

Recover Data and Visualize Result DataFrame

• gridds stores data from each run in an outputs directory titled by the run number and the experiment name.

• Here we recover the basic stats of the current run such as MAE and RMSE

# recover results for path run 0
base_path = os.path.join('outputs','interpolation_viz', '0')
df_path = os.path.join(base_path,'results.csv')
exp_name = base_path.split('/')[-2]
df = pd.read_csv(df_path)
df

	method_name	mae	rmse
0	KNN	0.252573	0.46692
1	Bayesian Ridge	0.252573	0.46692
2	Random Forest	0.252573	0.46692

viz_tools.visualize_output(os.path.join('outputs', exp.name))

Visualize Interpolation

• To save PDF/PNG versions of forecasts and other autoregression outputs one can just use the viz_tools.visualize_output functionality from the visualization library.

  • This will save outputs directly into outputs/interpolation_viz folder.

• Alternatively, this notebook will walkthrough this plotting procedure.

• Here we will plot each methods forecasts as a subplot.

# set up figure to have the correct number of subplots for each method
num_methods = len([elem for elem in os.listdir(base_path) if os.path.isdir(os.path.join(base_path,elem))])
methods_fig = plt.figure(figsize=(8,5))
methods_ax = methods_fig.gca()
# set "run_num" = 0 since we did not do cross validation
run_num = 0
method_ix = 0

for method in os.listdir(base_path):
    if 'results' in method: continue # dont parse results csv
    method_dir = os.path.join(base_path, method)
    curr_data = viz_tools.load_method(base_path, method, run_num)  # load data
    if not curr_data: continue
    fig = viz_tools.plot_result(df, curr_data, method_dir, ax=methods_ax)

# title axes
methods_ax.set_xlabel('time (s)', fontsize=22)
methods_ax.set_ylabel('kw', fontsize=22)
methods_ax.plot(curr_data['ground_truth'][:,0], label='ground_truth', color='black', linewidth=2, zorder=-10)
methods_ax.legend()

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