Tabular classification¶

• Supervised credit-g classification

In [ ]:

import torch.nn
import torch.optim

import openml
import openml_pytorch
import openml_pytorch.layers
import openml_pytorch.config
import logging


############################################################################
# Enable logging in order to observe the progress while running the example.
openml.config.logger.setLevel(logging.DEBUG)
openml_pytorch.config.logger.setLevel(logging.DEBUG)
############################################################################

import torch.nn import torch.optim import openml import openml_pytorch import openml_pytorch.layers import openml_pytorch.config import logging ############################################################################ # Enable logging in order to observe the progress while running the example. openml.config.logger.setLevel(logging.DEBUG) openml_pytorch.config.logger.setLevel(logging.DEBUG) ############################################################################

In [ ]:

from openml_pytorch.trainer import OpenMLTrainerModule
from openml_pytorch.trainer import OpenMLDataModule
from openml_pytorch.trainer import Callback

from openml_pytorch.trainer import OpenMLTrainerModule from openml_pytorch.trainer import OpenMLDataModule from openml_pytorch.trainer import Callback

Define the 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

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

In [ ]:

model = TabularClassificationmodel(20, 2)

model = TabularClassificationmodel(20, 2)

In [ ]:

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

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

Configure the Data Module¶

• Make sure the target_col is correctly set.

In [ ]:

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

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

Configure the Trainer Module¶

In [ ]:

trainer = OpenMLTrainerModule(
    data_module=data_module,
    verbose = True,
    epoch_count = 5,
)
openml_pytorch.config.trainer = trainer

trainer = OpenMLTrainerModule( data_module=data_module, verbose = True, epoch_count = 5, ) openml_pytorch.config.trainer = trainer

Run the model on the task¶

In [ ]:

run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

run = openml.runs.run_model_on_task(model, task, avoid_duplicate_runs=False)

In [ ]:

run.publish()

run.publish()

In [ ]:

# openml.config.apikey = ''

# openml.config.apikey = ''

View loss¶

In [ ]:

trainer.runner.cbs[1].plot_loss()

trainer.runner.cbs[1].plot_loss()

View learning rate¶

In [ ]:

trainer.runner.cbs[1].plot_lr()

trainer.runner.cbs[1].plot_lr()