Logistic Regression

Class Reference

class pykitml.LogisticRegression(input_size, output_size, reg_param=0)

Implements logistic regression for classification.

__init__(input_size, output_size, reg_param=0)
Parameters:

  • input_size (int) – Size of input data or number of input features.

  • output_size (int) – Number of categories or groups.

  • reg_param (int) – Regularization parameter for the model, also known as ‘weight decay’.

feed(input_data)

Accepts input array and feeds it to the model.

Parameters:

input_data (numpy.array) – The input to feed the model.

Raises:

ValueError – If the input data has invalid dimensions/shape.

Note

This function only feeds the input data, to get the output after calling this function use get_output() or get_output_onehot()

get_output()

Returns the output activations of the model.

Returns:

The output activations.

Return type:

numpy.array

get_output_onehot()

Returns the output layer activations of the model as a one-hot array. A one-hot array is an array of bits in which only one of the bits is high/true. In this case, the corresponding bit to the neuron/node having the highest activation will be high/true.

Returns:

The one-hot output activations array.

Return type:

numpy.array

train(training_data, targets, batch_size, epochs, optimizer, testing_data=None, testing_targets=None, testing_freq=1, decay_freq=1)

Trains the model on the training data, after training is complete, you can call plot_performance() to plot performance graphs.

Parameters:

  • training_data (numpy.array) – numpy array containing training data.

  • targets (numpy.array) – numpy array containing training targets, corresponding to the training data.

  • batch_size (int) – Number of training examples to use in one epoch, or number of training examples to use to estimate the gradient.

  • epochs (int) – Number of epochs the model should be trained for.

  • optimizer (any Optimizer object) – See Optimizers

  • testing_data (numpy.array) – numpy array containing testing data.

  • testing_targets (numpy.array) – numpy array containing testing targets, corresponding to the testing data.

  • testing_freq (int) – How frequently the model should be tested, i.e the model will be tested after every testing_freq epochs. You may want to increase this to reduce training time.

  • decay_freq (int) – How frequently the model should decay the learning rate. The learning rate will decay after every decay_freq epochs.

Raises:

ValueError – If training_data, targets, testing_data or testing_targets has invalid dimensions/shape.

plot_performance()

Plots logged performance data after training. Should be called after train().

Raises:

  • AttributeError – If the model has not been trained, i.e train() has not been called before.

  • IndexError – If train() failed.

cost(testing_data, testing_targets)

Tests the average cost of the model on the testing data passed to the function.

Parameters:

  • testing_data (numpy.array) – numpy array containing testing data.

  • testing_targets (numpy.array) – numpy array containing testing targets, corresponding to the testing data.

Returns:

cost – The average cost of the model over the testing data.

Return type:

float

Raises:

ValueError – If testing_data or testing_targets has invalid dimensions/shape.

accuracy(testing_data, testing_targets)

Tests the accuracy of the model on the testing data passed to the function. This function should be only used for classification.

Parameters:

  • testing_data (numpy.array) – numpy array containing testing data.

  • testing_targets (numpy.array) – numpy array containing testing targets, corresponding to the testing data.

Returns:

accuracy – The accuracy of the model over the testing data i.e how many testing examples did the model predict correctly.

Return type:

float

confusion_matrix(test_data, test_targets, gnames=[], plot=True)

Returns and plots confusion matrix on the given test data.

Parameters:

  • test_data (numpy.array) – Numpy array containing test data

  • test_targets (numpy.array) – Numpy array containing the targets corresponding to the test data.

  • plot (bool) – If set to false, will not plot the matrix. Default is true.

  • gnames (list) – List of string names for each class/group.

Returns:

confusion_matrix – The confusion matrix.

Return type:

numpy.array

Example: Banknote Authentication

Dataset

Banknote - pykitml.datasets.banknote module

Training

import os.path

import pykitml as pk
from pykitml.datasets import banknote

# Download the dataset
if not os.path.exists('banknote.pkl'):
    banknote.get()

# Load banknote data set
inputs_train, outputs_train, inputs_test, outputs_test = banknote.load()

# Normalize dataset
array_min, array_max = pk.get_minmax(inputs_train)
inputs_train = pk.normalize_minmax(inputs_train, array_min, array_max)
inputs_test = pk.normalize_minmax(inputs_test, array_min, array_max)

# Create polynomial features
inputs_train_poly = pk.polynomial(inputs_train)
inputs_test_poly = pk.polynomial(inputs_test)

# Create model
banknote_classifier = pk.LogisticRegression(inputs_train_poly.shape[1], 1)

# Train the model
banknote_classifier.train(
    training_data=inputs_train_poly,
    targets=outputs_train,
    batch_size=10,
    epochs=1500,
    optimizer=pk.Adam(learning_rate=0.06, decay_rate=0.99),
    testing_data=inputs_test_poly,
    testing_targets=outputs_test,
    testing_freq=30,
    decay_freq=40
)

# Save it
pk.save(banknote_classifier, 'banknote_classifier.pkl')

# Plot performance
banknote_classifier.plot_performance()

# Print accuracy
accuracy = banknote_classifier.accuracy(inputs_train_poly, outputs_train)
print('Train accuracy:', accuracy)
accuracy = banknote_classifier.accuracy(inputs_test_poly, outputs_test)
print('Test accuracy:', accuracy)

# Plot confusion matrix
banknote_classifier.confusion_matrix(inputs_test_poly, outputs_test)

Predict banknote validity with variance, skewness, curtosis, entropy: -2.3, -9.3, 9.37, -0.86

import numpy as np
import pykitml as pk
from pykitml.datasets import banknote

# Predict banknote validity with variance, skewness, curtosis, entropy
# of -2.3, -9.3, 9.37, -0.86

# Load banknote data set
inputs_train, _, _, _ = banknote.load()

# Load the model
banknote_classifier = pk.load('banknote_classifier.pkl')

# Normalize the inputs
array_min, array_max = pk.get_minmax(inputs_train)
input_data = pk.normalize_minmax(np.array([-2.3, -9.3, 9.37, -0.86]), array_min, array_max)

# Create polynomial features
input_data_poly = pk.polynomial(input_data)

# Get output
banknote_classifier.feed(input_data_poly)
model_output = banknote_classifier.get_output()

# Print result
print(model_output)

Performance Graph

_images/logistic_regression_perf_graph.png

Confusion Matrix

_images/logistic_regression_confusion_matrix.png