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regression_torchModel_example.py
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"""
Author : Abdullah Al Masud\n
email : [email protected]\n
LICENSE : MIT License
"""
# torchModel() regression example
import pandas as pd
from sklearn.datasets import fetch_california_housing
import torch
import os
import sys
project_dir = os.getcwd()
sys.path.append(project_dir)
from msdlib import mlutils
from msdlib import msd
# Loading the data and separating data and label
source_data = fetch_california_housing()
feature_names = source_data['feature_names'].copy()
data = pd.DataFrame(source_data['data'], columns=feature_names)
label = pd.Series(source_data['target'], name=source_data['target_names'][0])
# print(source_data['DESCR'])
print('data:\n', data.head())
# Standardizing numerical data
data = msd.standardize(data)
# Splitting data set into train, validation and test
splitter = msd.SplitDataset(data, label, test_ratio=.1)
outdata = splitter.random_split(val_ratio=.1)
print('outdata.keys() :', outdata.keys())
print("outdata['train'].keys() :", outdata['train'].keys())
print("outdata['validation'].keys() :", outdata['validation'].keys())
print("outdata['test'].keys() :", outdata['test'].keys())
print('train > data, labels and index shapes :',
outdata['train']['data'].shape, outdata['train']['label'].shape, outdata['train']['index'].shape)
# defining layers inside a list
layers = mlutils.define_layers(data.shape[1], 1, [100, 100, 100, 100, 100, 100], dropout_rate=.2, model_type='regressor',
actual_units=True, activation=torch.nn.ReLU())
# building model
tmodel = mlutils.torchModel(layers=layers, model_type='regressor', tensorboard_path='runs', interval=120,
savepath='examples/regression_torchModel', epoch=150, learning_rate=.0001, lr_reduce=.995)
# Training Pytorch model
train_set = mlutils.DataSet(torch.tensor(outdata['train']['data']), torch.tensor(outdata['train']['label']).squeeze(), dtype=torch.float32)
val_set = mlutils.DataSet(torch.tensor(outdata['validation']['data']), torch.tensor(outdata['validation']['label']).squeeze(), dtype=torch.float32)
test_set = mlutils.DataSet(torch.tensor(outdata['test']['data']), torch.tensor(outdata['test']['label']).squeeze(), dtype=torch.float32)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=128)
val_loader = torch.utils.data.DataLoader(val_set, batch_size=128)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=128)
tmodel.fit(train_loader=train_loader, val_loader=val_loader)
model_dict = {tmodel.model_name: tmodel.model}
model_dict = mlutils.load_models(model_dict, 'examples/regression_torchModel')
del tmodel.model
tmodel.model = model_dict[tmodel.model_name]
# Evaluating the model's performance
result, all_results = tmodel.evaluate(data_sets=[outdata['train']['data'], outdata['test']['data']],
label_sets=[outdata['train']['label'].ravel(
), outdata['test']['label'].ravel()],
set_names=['Train', 'Test'], savepath='examples/regression_torchModel')
result, all_results = tmodel.evaluate(data_sets=[train_loader, val_loader, test_loader],
set_names=['Train', 'Validation', 'Test'],
savepath='examples/regression_torchModel')
print('regression result:\n', result)
assert result['r_square'].loc['pytorch_Test'] >= .73, 'test set R-square is less than .73'