Pytorch-Data-Science-Using-Breast-Cancer-Example-Data-in-Python

Using sklearn's open source breast cancer dataset for datascience with Pytorch in Python:

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()

and splitting the training and testing numpy inputs matrix and target output labels:

x = data.data
y = data.target
import numpy as np
def split_data(x, y):
    _split_ones = np.round(np.where(y == 1.0)[0].shape[0]*0.5, decimals=0).astype(int)
    _split_zeros = np.round(np.where(y == 0.0)[0].shape[0]*0.5, decimals=0).astype(int)
    training_indexes = np.concatenate([np.where(y == 1.0)[0][:_split_ones], np.where(y == 0.0)[0][:_split_zeros]], axis=0)
    testing_indexes = np.concatenate([np.where(y == 1.0)[0][_split_ones:], np.where(y == 0.0)[0][_split_zeros:]], axis=0)
    return x[training_indexes, :], y[training_indexes], x[testing_indexes, :], y[testing_indexes]

_x_train, _y_train, _x_test, _y_test = split_data(x, y)

Pytorch provides an optimal environment for constructing neural network weights matrixes and conducting machine learning datascience; we start with constructing a class for the torch module:

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

class Model(nn.Module):
    def __init__(self, input_dimensions, hidden_dimensions, output_labels, feature_names, input_layer=None, output_layer=None):
        super().__init__()
        self.hidden_dimensions = hidden_dimensions
        self.output_labels = output_labels
        self.feature_names = feature_names
        self.input_layer = nn.Linear(input_dimensions, self.hidden_dimensions) if input_layer is None else input_layer
        self.output_layer = nn.Linear(self.hidden_dimensions, np.unique(self.output_labels).shape[0]) if output_layer is None else output_layer

    def forward(self, x):
        return F.softmax(self.output_layer(F.relu(self.input_layer(x))), dim=1)

    def get_weights(self):
        return self.input_layer.weight.detach().numpy(), self.output_layer.weight.detach().numpy()

    def find_significant_features(self, top_num_features=1):
        w, _ = self.get_weights()
        max_contribution_per_feature = np.reshape(np.max(w, axis=1), [w.shape[0]])
        return np.argsort(max_contribution_per_feature)[-top_num_features:]

model = Model(_x_train.shape[1], hidden_dimensions=30, output_labels=_y_train, feature_names=data['feature_names'])
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_fn = nn.CrossEntropyLoss()

training_iterations  = 1000
x_train = Variable(torch.from_numpy(_x_train)).float()
y_train = Variable(torch.from_numpy(_y_train)).long()
x_test = Variable(torch.from_numpy(_x_test)).float()
y_test = Variable(torch.from_numpy(_y_test)).long()

loss_list = np.zeros(training_iterations).astype(float)
accuracy_list = np.zeros(training_iterations).astype(float)

for _iteration in range(training_iterations):
    y_pred = model(x_train)
    loss = loss_fn(y_pred, y_train)
    loss_list[_iteration] = loss.item()

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    with torch.no_grad():
        y_pred = model(x_test)
        correct = (torch.argmax(y_pred, dim=1) == y_test).type(torch.FloatTensor)
        accuracy_list[_iteration] = correct.mean()

Two tensors which we'll call input_layer and output_layer, both of which also compose the hidden units as their second dimension which in this example we'll use size 30:

After 1,000 training iterations:

with the most contributing feature being "mean concave points":

Now this isn't a very large dataset and we can't draw direct conclusions just because the weights representing the input features that had the highest values in the input_layer tensor; further research must be conducted to uncover potential links between features such as "mean concave points" and breast cancer diagnoses.

Though if we plot the patients diagnosed with breast cancer (Red) and patients without breast cancer (Blue) for the 30 input features:

We can see that "mean concave points" feature shows significant differences for diagnoses.

[to be continued]