Tabular classification¶

• Supervised credit-g classification

In [ ]:

# openml imports
import openml
import openml_pytorch as op
from openml_pytorch.callbacks import TestCallback
from openml_pytorch.metrics import accuracy

# pytorch imports
import torch

# other imports
import logging
import warnings

# set up logging
openml.config.logger.setLevel(logging.DEBUG)
op.config.logger.setLevel(logging.DEBUG)
warnings.simplefilter(action='ignore')

# openml imports import openml import openml_pytorch as op from openml_pytorch.callbacks import TestCallback from openml_pytorch.metrics import accuracy # pytorch imports import torch # other imports import logging import warnings # set up logging openml.config.logger.setLevel(logging.DEBUG) op.config.logger.setLevel(logging.DEBUG) warnings.simplefilter(action='ignore')

Data¶

Define image transformations¶

Configure the Data Module and Choose a Task¶

• Make sure the data is present in the file_dir directory, and the filename_col is correctly set along with this column correctly pointing to where your data is stored.

In [ ]:

data_module = op.OpenMLDataModule(
    type_of_data="dataframe",
    target_column="class",
    target_mode="categorical",
)

# supervised credit-g classification
task = openml.tasks.get_task(31)

data_module = op.OpenMLDataModule( type_of_data="dataframe", target_column="class", target_mode="categorical", ) # supervised credit-g classification task = openml.tasks.get_task(31)

Model¶

In [ ]:

class TabularClassificationmodel(torch.nn.Module):
    def __init__(self, input_size, output_size):
        super(TabularClassificationmodel, self).__init__()
        self.fc1 = torch.nn.Linear(input_size, 128)
        self.fc2 = torch.nn.Linear(128, 64)
        self.fc3 = torch.nn.Linear(64, output_size)
        self.relu = torch.nn.ReLU()
        self.softmax = torch.nn.Softmax(dim=1)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        x = self.softmax(x)
        return x
    
model = TabularClassificationmodel(20, 2)

class TabularClassificationmodel(torch.nn.Module): def __init__(self, input_size, output_size): super(TabularClassificationmodel, self).__init__() self.fc1 = torch.nn.Linear(input_size, 128) self.fc2 = torch.nn.Linear(128, 64) self.fc3 = torch.nn.Linear(64, output_size) self.relu = torch.nn.ReLU() self.softmax = torch.nn.Softmax(dim=1) def forward(self, x): x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.relu(x) x = self.fc3(x) x = self.softmax(x) return x model = TabularClassificationmodel(20, 2)

Train your model on the data¶

• Note that by default, OpenML runs a 10 fold cross validation on the data. You cannot change this for now.

In [ ]:

trainer = op.OpenMLTrainerModule(
    experiment_name= "Credit-G",
    data_module=data_module,
    verbose=True,
    epoch_count=2,
    metrics= [accuracy],
    # remove the TestCallback when you are done testing your pipeline. Having it here will make the pipeline run for a very short time.
    callbacks=[
        # TestCallback,
    ],
    opt = torch.optim.Adam,
)
op.config.trainer = trainer
run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

trainer = op.OpenMLTrainerModule( experiment_name= "Credit-G", data_module=data_module, verbose=True, epoch_count=2, metrics= [accuracy], # remove the TestCallback when you are done testing your pipeline. Having it here will make the pipeline run for a very short time. callbacks=[ # TestCallback, ], opt = torch.optim.Adam, ) op.config.trainer = trainer run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

View information about your run¶

Learning rate and loss plot¶

In [ ]:

trainer.plot_loss()

trainer.plot_loss()

In [ ]:

trainer.plot_lr()

trainer.plot_lr()

Class labels¶

In [ ]:

trainer.model_classes

trainer.model_classes

Model Vizualization¶

• Sometimes you may want to visualize the model. You can either use netron or tensorboard for this purpose.

Netron¶

In [ ]:

trainer.export_to_netron()

trainer.export_to_netron()

Tensorboard¶

• By default, openml will log the tensorboard logs in the tensorboard_logs directory. You can view the logs by running tensorboard --logdir tensorboard_logs in the terminal.

Publish your model to OpenML¶

• This is Optional, but publishing your model to OpenML will allow you to track your experiments and compare them with others.
• Make sure to set your apikey first.
  • You can find your apikey on your OpenML account page.

In [ ]:

openml.config.apikey = ''
run = op.add_experiment_info_to_run(run=run, trainer=trainer) 
run.publish()

openml.config.apikey = '' run = op.add_experiment_info_to_run(run=run, trainer=trainer) run.publish()