import sys sys.path.append('..') from helpers import load_data_in_chunks, load_model, report_results (Xs, Ys) = load_data_in_chunks('test', chunk_size=5) Xs = Xs.reshape(Xs.shape[0], -1) regr = load_model('linear-multiple') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'linear', 'linear-multiple.svg')
import sys sys.path.append('..') from helpers import load_data_in_chunks, load_model, report_results (Xs, Ys) = load_data_in_chunks('basic', 'test', chunk_size=5) Xs = Xs.astype('float32') Ys = Ys.astype('float32') regr = load_model('lstm-mse') Ys_pred = regr.predict(Xs) * 5000 (a, b, c) = Xs.shape report_results(Xs.reshape(a * b, c), Ys, Ys_pred, 'MSE LSTM', 'lstm-mse.svg')
import sys sys.path.append('..') from helpers import load_data, load_model, report_results (Xs, Ys) = load_data('basic', 'test') regr = load_model('random-forest') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'random forest', 'rf.svg')
import sys sys.path.append('..') from helpers import load_data_in_chunks, load_model, report_results (Xs, Ys) = load_data_in_chunks('basic', 'test', chunk_size=5) Xs = Xs.astype('float32') Ys = Ys.astype('float32') regr = load_model('conv-custom') Ys_pred = regr.predict(Xs) * 5000 (a, b, c) = Xs.shape report_results(Xs.reshape(a * b, c), Ys, Ys_pred, 'convolutional', 'conv-custom.svg')
import sys sys.path.append('..') from helpers import load_data_in_chunks, load_model, report_results (Xs, Ys) = load_data_in_chunks('test', chunk_size=5) Xs = Xs.astype('float32') Ys = Ys.astype('float32') regr = load_model('lstm-custom') Ys_pred = regr.predict(Xs) * 5000 (a, b, c) = Xs.shape report_results(Xs.reshape(a * b, c), Ys, Ys_pred, 'LSTM', 'lstm-custom.svg')
import sys sys.path.append('..') from helpers import load_data, load_model, report_results (Xs, Ys) = load_data('test') regr = load_model('linear-basic') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'linear', 'linear.svg')
import sys sys.path.append('..') from helpers import load_data, load_model, report_results (Xs, Ys) = load_data('basic', 'test') regr = load_model('dummy') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'dummy', 'dummy.svg')
import sys sys.path.append('..') from helpers import load_data_in_chunks, load_model, report_results (Xs, Ys) = load_data_in_chunks('test', chunk_size=5) Xs = Xs.astype('float32') Ys = Ys.astype('float32') regr = load_model('conv-mse') Ys_pred = regr.predict(Xs) * 5000 (a, b, c) = Xs.shape report_results(Xs.reshape(a * b, c), Ys, Ys_pred, 'convolutional MSE', 'conv-mse.svg')
import sys sys.path.append('..') from helpers import load_data, load_model, report_results import numpy as np (Xs, Ys) = load_data('test') regr = load_model('mlp') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'multi-layer perceptron', 'mlp.svg')
import sys sys.path.append('..') from helpers import load_data, load_model, report_results (Xs, Ys) = load_data('basic', 'test') regr = load_model('xgb') Ys_pred = regr.predict(Xs) report_results(Xs, Ys, Ys_pred, 'gradient boosted', 'xgboost.svg')