guess the correlation

data inspection

a pretty normal distribution

train/val/test split

splitting amount

.dataset:                150000 instances
├─80%─├─80%─training      96000 instances
│     └─20%─validation    24000 instances
├─20%─testing             30000 instances

after a rough glance at the dataset distribution, considered the dataset is pretty normal distributed and has enough instances to keep the variance low after 80/20 splitting.

splitting method

def _split_dataset(self, split, training=True):
    if split == 0.0:
        return None, None

    # self.correlations_frame = pd.read_csv('path/to/csv_file')
    n_samples = len(self.correlations_frame)

    idx_full = np.arange(n_samples)

    # fix seed for referenceable testing set
    np.random.seed(0)
    np.random.shuffle(idx_full)

    if isinstance(split, int):
        assert split > 0
        assert split < n_samples, "testing set size is configured to be larger than entire dataset."
        len_test = split
    else:
        len_test = int(n_samples * split)

    test_idx = idx_full[0:len_test]
    train_idx = np.delete(idx_full, np.arange(0, len_test))

    if training:
        dataset = self.correlations_frame.ix[train_idx]
    else:
        dataset = self.correlations_frame.ix[test_idx]

    return dataset

training/validation splitting uses the same logic

model inspection

CorrelationModel(
  (features): Sequential(
    (0): Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))
    #(0): params: (3*3*1+1) * 16 = 160
    (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    #(1): params: 16 * 2 = 32
    (2): ReLU(inplace=True)
    (3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2))
    #(4): params: (3*3*16+1) * 32 = 4640
    (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    #(5): params: 32 * 2 = 64
    (6): ReLU(inplace=True)
    (7): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (8): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    #(8): params: (3*3*32+1) * 64 = 18496
    (9): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    #(9): params: 64 * 2 = 128
    (10): ReLU(inplace=True)
    (11): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (12): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    #(12): params: (3*3*64+1) * 32 = 18464
    (13): ReLU(inplace=True)
    (14): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (15): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (#15): params: (3*3*32+1) * 16 = 4624
    (16): ReLU(inplace=True)
    (17): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (18): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (#18): params: (3*3*16+1) * 8 = 1160
    (19): ReLU(inplace=True)
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (linear): Sequential(
    (0): Conv2d(8, 1, kernel_size=(1, 1), stride=(1, 1))
    #(0): params: (8+1) * 1 = 9
    (1): Tanh()
  )
)
Trainable parameters: 47777

loss function

the loss function of choice is smooth_l1, which has the advantages of both l1 and l2 loss

def SmoothL1(yhat, y):                                                  <--- final choice
    return torch.nn.functional.smooth_l1_loss(yhat, y)

def MSELoss(yhat, y):
    return torch.nn.functional.mse_loss(yhat, y)

def RMSELoss(yhat, y):
    return torch.sqrt(MSELoss(yhat, y))

def MSLELoss(yhat, y):
    return MSELoss(torch.log(yhat + 1), torch.log(y + 1))

def RMSLELoss(yhat, y):
    return torch.sqrt(MSELoss(torch.log(yhat + 1), torch.log(y + 1)))

evaluation metric

def mse(output, target):
    # mean square error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        mae = torch.sum(MSELoss(output, target)).item()
    return mae / len(target)

def mae(output, target):
    # mean absolute error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        mae = torch.sum(abs(target-output)).item()
    return mae / len(target)

def mape(output, target):
    # mean absolute percentage error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        mape = torch.sum(abs((target-output)/target)).item()
    return mape / len(target)

def rmse(output, target):
    # root mean square error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        rmse = torch.sum(torch.sqrt(MSELoss(output, target))).item()
    return rmse / len(target)

def msle(output, target):
    # mean square log error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        msle = torch.sum(MSELoss(torch.log(output + 1), torch.log(target + 1))).item()
    return msle / len(target)

def rmsle(output, target):
    # root mean square log error
    with torch.no_grad():
        assert output.shape[0] == len(target)
        rmsle = torch.sum(torch.sqrt(MSELoss(torch.log(output + 1), torch.log(target + 1)))).item()
    return rmsle / len(target)

training result

trainer - INFO -     epoch          : 1
trainer - INFO -     smooth_l1loss  : 0.0029358651146370296
trainer - INFO -     mse            : 9.174910654958997e-05
trainer - INFO -     mae            : 0.04508562459920844
trainer - INFO -     mape           : 0.6447089369893074
trainer - INFO -     rmse           : 0.0008826211761528006
trainer - INFO -     msle           : 0.0002885178522810747
trainer - INFO -     rmsle          : 0.0016459243478796756
trainer - INFO -     val_loss       : 0.000569225614812846
trainer - INFO -     val_mse        : 1.7788300462901436e-05
trainer - INFO -     val_mae        : 0.026543946107228596
trainer - INFO -     val_mape       : 0.48582320946455004
trainer - INFO -     val_rmse       : 0.0005245986936303476
trainer - INFO -     val_msle       : 9.091730712680146e-05
trainer - INFO -     val_rmsle      : 0.0009993902465794235
                    .
                    .
                    .
                    .
                    .
                    .
trainer - INFO -     epoch          : 7                           <--- final model
trainer - INFO -     smooth_l1loss  : 0.00017805844737449661
trainer - INFO -     mse            : 5.564326480453019e-06
trainer - INFO -     mae            : 0.01469234253714482
trainer - INFO -     mape           : 0.2645472921580076
trainer - INFO -     rmse           : 0.0002925463738307978
trainer - INFO -     msle           : 3.3151906652316634e-05
trainer - INFO -     rmsle          : 0.0005688522928685416
trainer - INFO -     val_loss       : 0.00017794455110561102
trainer - INFO -     val_mse        : 5.560767222050344e-06
trainer - INFO -     val_mae        : 0.014510956528286139
trainer - INFO -     val_mape       : 0.25059283276398975
trainer - INFO -     val_rmse       : 0.0002930224982944007
trainer - INFO -     val_msle       : 3.403802761204133e-05
trainer - INFO -     val_rmsle      : 0.0005525556141122554
trainer - INFO - Saving checkpoint: saved/models/correlation/1031_043742/checkpoint-epoch7.pth ...
trainer - INFO - Saving current best: model_best.pth ...
                    .
                    .
                    .
                    .
                    .
                    .
trainer - INFO -     epoch          : 10                           <--- early stop
trainer - INFO -     smooth_l1loss  : 0.00014610137016279624
trainer - INFO -     mse            : 4.565667817587382e-06
trainer - INFO -     mae            : 0.013266990386570494
trainer - INFO -     mape           : 0.24146838792661826
trainer - INFO -     rmse           : 0.00026499629460158757
trainer - INFO -     msle           : 2.77259079665176e-05
trainer - INFO -     rmsle          : 0.0005148174095957074
trainer - INFO -     val_loss       : 0.00018394086218904705
trainer - INFO -     val_mse        : 5.74815194340772e-06
trainer - INFO -     val_mae        : 0.01494487459709247
trainer - INFO -     val_mape       : 0.27262411576509477
trainer - INFO -     val_rmse       : 0.0002979971170425415
trainer - INFO -     val_msle       : 3.1850282267744966e-05
trainer - INFO -     val_rmsle      : 0.0005451643197642019
trainer - INFO - Validation performance didn't improve for 2 epochs. Training stops.

loss graph

testing result

Loading checkpoint: saved/models/correlation/model_best.pth ...
Done
Testing set samples: 30000
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 59/59 [00:19<00:00,  3.04it/s]
Testing result:
{'loss': 0.0001722179292468354, 'mse': 6.77461177110672e-07, 'mae': 0.014289384969075522, 'mape': 0.2813985677083333, 'rmse': 3.6473782857259115e-05, 'msle': 3.554690380891164e-06, 'rmsle': 7.881066799163819e-05}

take home quiz

Related tags

Overview

guess the correlation

data inspection

train/val/test split

model inspection

loss function

evaluation metric

training result

testing result

Owner

HR Wu

Automatically find solutions when your Python code encounters an issue.

KUIZ is a web application quiz where you can create/take a quiz for learning and sharing knowledge from various subjects, questions and answers.

A Python 3 client for the beanstalkd work queue

Traits for Python3

This is a Python script to detect rapid upwards price changes (pumps) in a cryptocurrency pairing

Lagrange Interpolation Method-Python

This is an implementation of PEP 557, Data Classes.

Бэкапалка таблиц mysql 8 через брокер сообщений nats

Why write code when you can import it directly from GitHub Copilot?

Aerospace utilities: flight conditions package, standard atmosphere model, and more.

The blancmange curve can be visually built up out of triangle wave functions if the infinite sum is approximated by finite sums of the first few terms.

A general illumination correction method for optical microscopy.

More routines for operating on iterables, beyond itertools

Backup dc registry - A simple POC that abuses Backup Operator privileges to remote dump SAM, SYSTEM, and SECURITY

[Cython] Vs [Python] Which one is Faster ?

Randomly distribute members by groups making sure that every sector is represented

A python script that fetches the grades of a student from a WAEC result in pdf format.

Convert long numbers into a human-readable format in Python

An OpenSource crowd-sourced cooking recipes website

A passive recon suite designed for fetching the information about web application