The post Develop k-Nearest Neighbors in Python From Scratch appeared first on Machine Learning Mastery.

]]>A simple but powerful approach for making predictions is to use the most similar historical examples to the new data. This is the principle behind the k-Nearest Neighbors algorithm.

After completing this tutorial you will know:

- How to code the k-Nearest Neighbors algorithm step-by-step.
- How to evaluate k-Nearest Neighbors on a real dataset.
- How to use k-Nearest Neighbors to make a prediction for new data.

Discover how to code ML algorithms from scratch including kNN, decision trees, neural nets, ensembles and much more in my new book, with full Python code and no fancy libraries.

Let’s get started.

**Updated Sep/2014**: Original version of the tutorial.**Updated Oct/2019**: Complete rewritten from the ground up.

This section will provide a brief background on the k-Nearest Neighbors algorithm that we will implement in this tutorial and the Abalone dataset to which we will apply it.

The k-Nearest Neighbors algorithm or KNN for short is a very simple technique.

The entire training dataset is stored. When a prediction is required, the k-most similar records to a new record from the training dataset are then located. From these neighbors, a summarized prediction is made.

Similarity between records can be measured many different ways. A problem or data-specific method can be used. Generally, with tabular data, a good starting point is the Euclidean distance.

Once the neighbors are discovered, the summary prediction can be made by returning the most common outcome or taking the average. As such, KNN can be used for classification or regression problems.

There is no model to speak of other than holding the entire training dataset. Because no work is done until a prediction is required, KNN is often referred to as a lazy learning method.

In this tutorial we will use the Iris Flower Species Dataset.

The Iris Flower Dataset involves predicting the flower species given measurements of iris flowers.

It is a multiclass classification problem. The number of observations for each class is balanced. There are 150 observations with 4 input variables and 1 output variable. The variable names are as follows:

- Sepal length in cm.
- Sepal width in cm.
- Petal length in cm.
- Petal width in cm.
- Class

A sample of the first 5 rows is listed below.

5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa ...

The baseline performance on the problem is approximately 33%.

Download the dataset and save it into your current working directory with the filename “*iris.csv*“.

First we will develop each piece of the algorithm in this section, then we will tie all of the elements together into a working implementation applied to a real dataset in the next section.

This k-Nearest Neighbors tutorial is broken down into 3 parts:

**Step 1**: Calculate Euclidean Distance.**Step 2**: Get Nearest Neighbors.**Step 3**: Make Predictions.

These steps will teach you the fundamentals of implementing and applying the k-Nearest Neighbors algorithm for classification and regression predictive modeling problems.

**Note**: This tutorial assumes that you are using Python 3. If you need help installing Python, see this tutorial:

I believe the code in this tutorial will also work with Python 2.7 without any changes.

The first step is to calculate the distance between two rows in a dataset.

Rows of data are mostly made up of numbers and an easy way to calculate the distance between two rows or vectors of numbers is to draw a straight line. This makes sense in 2D or 3D and scales nicely to higher dimensions.

We can calculate the straight line distance between two vectors using the Euclidean distance measure. It is calculated as the square root of the sum of the squared differences between the two vectors.

- Euclidean Distance = sqrt(sum i to N (x1_i – x2_i)^2)

Where *x1* is the first row of data, *x2* is the second row of data and *i* is the index to a specific column as we sum across all columns.

With Euclidean distance, the smaller the value, the more similar two records will be. A value of 0 means that there is no difference between two records.

Below is a function named *euclidean_distance()* that implements this in Python.

# calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance)

You can see that the function assumes that the last column in each row is an output value which is ignored from the distance calculation.

We can test this distance function with a small contrived classification dataset. We will use this dataset a few times as we construct the elements needed for the KNN algorithm.

X1 X2 Y 2.7810836 2.550537003 0 1.465489372 2.362125076 0 3.396561688 4.400293529 0 1.38807019 1.850220317 0 3.06407232 3.005305973 0 7.627531214 2.759262235 1 5.332441248 2.088626775 1 6.922596716 1.77106367 1 8.675418651 -0.242068655 1 7.673756466 3.508563011 1

Below is a plot of the dataset using different colors to show the different classes for each point.

Putting this all together, we can write a small example to test our distance function by printing the distance between the first row and all other rows. We would expect the distance between the first row and itself to be 0, a good thing to look out for.

The full example is listed below.

# Example of calculating Euclidean distance from math import sqrt # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Test distance function dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] row0 = dataset[0] for row in dataset: distance = euclidean_distance(row0, row) print(distance)

Running this example prints the distances between the first row and every row in the dataset, including itself.

0.0 1.3290173915275787 1.9494646655653247 1.5591439385540549 0.5356280721938492 4.850940186986411 2.592833759950511 4.214227042632867 6.522409988228337 4.985585382449795

Now it is time to use the distance calculation to locate neighbors within a dataset.

Neighbors for a new piece of data in the dataset are the *k* closest instances, as defined by our distance measure.

To locate the neighbors for a new piece of data within a dataset we must first calculate the distance between each record in the dataset to the new piece of data. We can do this using our distance function prepared above.

Once distances are calculated, we must sort all of the records in the training dataset by their distance to the new data. We can then select the top *k* to return as the most similar neighbors.

We can do this by keeping track of the distance for each record in the dataset as a tuple, sort the list of tuples by the distance (in descending order) and then retrieve the neighbors.

Below is a function named *get_neighbors()* that implements this.

# Locate the most similar neighbors def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors

You can see that the *euclidean_distance()* function developed in the previous step is used to calculate the distance between each *train_row* and the new *test_row*.

The list of *train_row* and distance tuples is sorted where a custom key is used ensuring that the second item in the tuple (*tup[1]*) is used in the sorting operation.

Finally, a list of the *num_neighbors* most similar neighbors to *test_row* is returned.

We can test this function with the small contrived dataset prepared in the previous section.

The complete example is listed below.

# Example of getting neighbors for an instance from math import sqrt # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the most similar neighbors def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Test distance function dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] neighbors = get_neighbors(dataset, dataset[0], 3) for neighbor in neighbors: print(neighbor)

Running this example prints the 3 most similar records in the dataset to the first record, in order of similarity.

As expected, the first record is the most similar to itself and is at the top of the list.

[2.7810836, 2.550537003, 0] [3.06407232, 3.005305973, 0] [1.465489372, 2.362125076, 0]

Now that we know how to get neighbors from the dataset, we can use them to make predictions.

The most similar neighbors collected from the training dataset can be used to make predictions.

In the case of classification, we can return the most represented class among the neighbors.

We can achieve this by performing the *max()* function on the list of output values from the neighbors. Given a list of class values observed in the neighbors, the *max()* function takes a set of unique class values and calls the count on the list of class values for each class value in the set.

Below is the function named *predict_classification()* that implements this.

# Make a classification prediction with neighbors def predict_classification(train, test_row, num_neighbors): neighbors = get_neighbors(train, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction

We can test this function on the above contrived dataset.

Below is a complete example.

# Example of making predictions from math import sqrt # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the most similar neighbors def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Make a classification prediction with neighbors def predict_classification(train, test_row, num_neighbors): neighbors = get_neighbors(train, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction # Test distance function dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] prediction = predict_classification(dataset, dataset[0], 3) print('Expected %d, Got %d.' % (dataset[0][-1], prediction))

Running this example prints the expected classification of 0 and the actual classification predicted from the 3 most similar neighbors in the dataset.

Expected 0, Got 0.

We can imagine how the *predict_classification()* function can be changed to calculate the mean value of the outcome values.

We now have all of the pieces to make predictions with KNN. Let’s apply it to a real dataset.

This section applies the KNN algorithm to the Iris flowers dataset.

The first step is to load the dataset and convert the loaded data to numbers that we can use with the mean and standard deviation calculations. For this we will use the helper function *load_csv()* to load the file, *str_column_to_float()* to convert string numbers to floats and *str_column_to_int()* to convert the class column to integer values.

We will evaluate the algorithm using k-fold cross-validation with 5 folds. This means that 150/5=30 records will be in each fold. We will use the helper functions *evaluate_algorithm()* to evaluate the algorithm with cross-validation and *accuracy_metric()* to calculate the accuracy of predictions.

A new function named *k_nearest_neighbors()* was developed to manage the application of the KNN algorithm, first learning the statistics from a training dataset and using them to make predictions for a test dataset.

If you would like more help with the data loading functions used below, see the tutorial:

If you would like more help with the way the model is evaluated using cross validation, see the tutorial:

The complete example is listed below.

# k-nearest neighbors on the Iris Flowers Dataset from random import seed from random import randrange from csv import reader from math import sqrt # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Find the min and max values for each column def dataset_minmax(dataset): minmax = list() for i in range(len(dataset[0])): col_values = [row[i] for row in dataset] value_min = min(col_values) value_max = max(col_values) minmax.append([value_min, value_max]) return minmax # Rescale dataset columns to the range 0-1 def normalize_dataset(dataset, minmax): for row in dataset: for i in range(len(row)): row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0]) # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for _ in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the most similar neighbors def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Make a prediction with neighbors def predict_classification(train, test_row, num_neighbors): neighbors = get_neighbors(train, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction # kNN Algorithm def k_nearest_neighbors(train, test, num_neighbors): predictions = list() for row in test: output = predict_classification(train, row, num_neighbors) predictions.append(output) return(predictions) # Test the kNN on the Iris Flowers dataset seed(1) filename = 'iris.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 5 num_neighbors = 5 scores = evaluate_algorithm(dataset, k_nearest_neighbors, n_folds, num_neighbors) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

Running the example prints the mean classification accuracy scores on each cross-validation fold as well as the mean accuracy score.

We can see that the mean accuracy of about 96.6% is dramatically better than the baseline accuracy of 33%.

Scores: [96.66666666666667, 96.66666666666667, 100.0, 90.0, 100.0] Mean Accuracy: 96.667%

We can use the training dataset to make predictions for new observations (rows of data).

This involves making a call to the *predict_classification()* function with a row representing our new observation to predict the class label.

... # predict the label label = predict_classification(dataset, row, num_neighbors)

We also might like to know the class label (string) for a prediction.

We can update the *str_column_to_int()* function to print the mapping of string class names to integers so we can interpret the prediction made by the model.

# Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i print('[%s] => %d' % (value, i)) for row in dataset: row[column] = lookup[row[column]] return lookup

Tying this together, a complete example of using KNN with the entire dataset and making a single prediction for a new observation is listed below.

# Make Predictions with k-nearest neighbors on the Iris Flowers Dataset from csv import reader from math import sqrt # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i print('[%s] => %d' % (value, i)) for row in dataset: row[column] = lookup[row[column]] return lookup # Find the min and max values for each column def dataset_minmax(dataset): minmax = list() for i in range(len(dataset[0])): col_values = [row[i] for row in dataset] value_min = min(col_values) value_max = max(col_values) minmax.append([value_min, value_max]) return minmax # Rescale dataset columns to the range 0-1 def normalize_dataset(dataset, minmax): for row in dataset: for i in range(len(row)): row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0]) # Calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the most similar neighbors def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Make a prediction with neighbors def predict_classification(train, test_row, num_neighbors): neighbors = get_neighbors(train, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction # Make a prediction with KNN on Iris Dataset filename = 'iris.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # define model parameter num_neighbors = 5 # define a new record row = [5.7,2.9,4.2,1.3] # predict the label label = predict_classification(dataset, row, num_neighbors) print('Data=%s, Predicted: %s' % (row, label))

Running the data first summarizes the mapping of class labels to integers and then fits the model on the entire dataset.

Then a new observation is defined (in this case I took a row from the dataset), and a predicted label is calculated.

In this case our observation is predicted as belonging to class 1 which we know is “*Iris-setosa*“.

[Iris-virginica] => 0 [Iris-setosa] => 1 [Iris-versicolor] => 2 Data=[4.5, 2.3, 1.3, 0.3], Predicted: 1

This section lists extensions to the tutorial you may wish to consider to investigate.

**Tune KNN**. Try larger and larger*k*values to see if you can improve the performance of the algorithm on the Iris dataset.**Regression**. Adapt the example and apply it to a regression predictive modeling problem (e.g. predict a numerical value)**More Distance Measures**. Implement other distance measures that you can use to find similar historical data, such as Hamming distance, Manhattan distance and Minkowski distance.**Data Preparation**. Distance measures are strongly affected by the scale of the input data. Experiment with standardization and other data preparation methods in order to improve results.**More Problems**. As always, experiment with the technique on more and different classification and regression problems.

- Section 3.5 Comparison of Linear Regression with K-Nearest Neighbors, page 104, An Introduction to Statistical Learning, 2014.
- Section 18.8. Nonparametric Models, page 737, Artificial Intelligence: A Modern Approach, 2010.
- Section 13.5 K-Nearest Neighbors, page 350 Applied Predictive Modeling, 2013
- Section 4.7, Instance-based learning, page 128, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, 2005.

In this tutorial you discovered how to implement the k-Nearest Neighbors algorithm from scratch with Python.

Specifically, you learned:

- How to code the k-Nearest Neighbors algorithm step-by-step.
- How to evaluate k-Nearest Neighbors on a real dataset.
- How to use k-Nearest Neighbors to make a prediction for new data.

Take action!

- Follow the tutorial and implement KNN from scratch.
- Adapt the example to another dataset.
- Follow the extensions and improve upon the implementation.

Leave a comment and share your experiences.

The post Develop k-Nearest Neighbors in Python From Scratch appeared first on Machine Learning Mastery.

]]>The post Naive Bayes Classifier From Scratch in Python appeared first on Machine Learning Mastery.

]]>We can use probability to make predictions in machine learning. Perhaps the most widely used example is called the Naive Bayes algorithm. Not only is it straightforward to understand, but it also achieves surprisingly good results on a wide range of problems.

After completing this tutorial you will know:

- How to calculate the probabilities required by the Naive Bayes algorithm.
- How to implement the Naive Bayes algorithm from scratch.
- How to apply Naive Bayes to a real-world predictive modeling problem.

Discover how to code ML algorithms from scratch including kNN, decision trees, neural nets, ensembles and much more in my new book, with full Python code and no fancy libraries.

Let’s get started.

**Update Dec/2014**: Original implementation.**Update Oct/2019**: Rewrote the tutorial and code from the ground-up.

This section provides a brief overview of the Naive Bayes algorithm and the Iris flowers dataset that we will use in this tutorial.

Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge. Bayes’ Theorem is stated as:

- P(class|data) = (P(data|class) * P(class)) / P(data)

Where P(class|data) is the probability of class given the provided data.

For an in-depth introduction to Bayes Theorem, see the tutorial:

Naive Bayes is a classification algorithm for binary (two-class) and multiclass classification problems. It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.

Rather than attempting to calculate the probabilities of each attribute value, they are assumed to be conditionally independent given the class value.

This is a very strong assumption that is most unlikely in real data, i.e. that the attributes do not interact. Nevertheless, the approach performs surprisingly well on data where this assumption does not hold.

For an in-depth introduction to Naive Bayes, see the tutorial:

In this tutorial we will use the Iris Flower Species Dataset.

The Iris Flower Dataset involves predicting the flower species given measurements of iris flowers.

It is a multiclass classification problem. The number of observations for each class is balanced. There are 150 observations with 4 input variables and 1 output variable. The variable names are as follows:

- Sepal length in cm.
- Sepal width in cm.
- Petal length in cm.
- Petal width in cm.
- Class

A sample of the first 5 rows is listed below.

5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa ...

The baseline performance on the problem is approximately 33%.

Download the dataset and save it into your current working directory with the filename *iris.csv*.

First we will develop each piece of the algorithm in this section, then we will tie all of the elements together into a working implementation applied to a real dataset in the next section.

This Naive Bayes tutorial is broken down into 5 parts:

- Step 1: Separate By Class.
- Step 2: Summarize Dataset.
- Step 3: Summarize Data By Class.
- Step 4: Gaussian Probability Density Function.
- Step 5: Class Probabilities.

These steps will provide the foundation that you need to implement Naive Bayes from scratch and apply it to your own predictive modeling problems.

**Note**: This tutorial assumes that you are using **Python 3**. If you need help installing Python, see this tutorial:

**Note**: if you are using **Python 2.7**, you must change all calls to the *items()* function on dictionary objects to *iteritems()*.

We will need to calculate the probability of data by the class they belong to, the so-called base rate.

This means that we will first need to separate our training data by class. A relatively straightforward operation.

We can create a dictionary object where each key is the class value and then add a list of all the records as the value in the dictionary.

Below is a function named *separate_by_class()* that implements this approach. It assumes that the last column in each row is the class value.

# Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated

We can contrive a small dataset to test out this function.

X1 X2 Y 3.393533211 2.331273381 0 3.110073483 1.781539638 0 1.343808831 3.368360954 0 3.582294042 4.67917911 0 2.280362439 2.866990263 0 7.423436942 4.696522875 1 5.745051997 3.533989803 1 9.172168622 2.511101045 1 7.792783481 3.424088941 1 7.939820817 0.791637231 1

We can plot this dataset and use separate colors for each class.

Putting this all together, we can test our *separate_by_class()* function on the contrived dataset.

# Example of separating data by class value # Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated # Test separating data by class dataset = [[3.393533211,2.331273381,0], [3.110073483,1.781539638,0], [1.343808831,3.368360954,0], [3.582294042,4.67917911,0], [2.280362439,2.866990263,0], [7.423436942,4.696522875,1], [5.745051997,3.533989803,1], [9.172168622,2.511101045,1], [7.792783481,3.424088941,1], [7.939820817,0.791637231,1]] separated = separate_by_class(dataset) for label in separated: print(label) for row in separated[label]: print(row)

Running the example sorts observations in the dataset by their class value, then prints the class value followed by all identified records.

0 [3.393533211, 2.331273381, 0] [3.110073483, 1.781539638, 0] [1.343808831, 3.368360954, 0] [3.582294042, 4.67917911, 0] [2.280362439, 2.866990263, 0] 1 [7.423436942, 4.696522875, 1] [5.745051997, 3.533989803, 1] [9.172168622, 2.511101045, 1] [7.792783481, 3.424088941, 1] [7.939820817, 0.791637231, 1]

Next we can start to develop the functions needed to collect statistics.

We need two statistics from a given set of data.

We’ll see how these statistics are used in the calculation of probabilities in a few steps. The two statistics we require from a given dataset are the mean and the standard deviation (average deviation from the mean).

The mean is the average value and can be calculated as:

- mean = sum(x)/n * count(x)

Where *x* is the list of values or a column we are looking.

Below is a small function named *mean()* that calculates the mean of a list of numbers.

# Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers))

The sample standard deviation is calculated as the mean difference from the mean value. This can be calculated as:

- standard deviation = sqrt((sum i to N (x_i – mean(x))^2) / N-1)

You can see that we square the difference between the mean and a given value, calculate the average squared difference from the mean, then take the square root to return the units back to their original value.

Below is a small function named *standard_deviation()* that calculates the standard deviation of a list of numbers. You will notice that it calculates the mean. It might be more efficient to calculate the mean of a list of numbers once and pass it to the *standard_deviation()* function as a parameter. You can explore this optimization if you’re interested later.

from math import sqrt # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance)

We require the mean and standard deviation statistics to be calculated for each input attribute or each column of our data.

We can do that by gathering all of the values for each column into a list and calculating the mean and standard deviation on that list. Once calculated, we can gather the statistics together into a list or tuple of statistics. Then, repeat this operation for each column in the dataset and return a list of tuples of statistics.

Below is a function named *summarize_dataset()* that implements this approach. It uses some Python tricks to cut down on the number of lines required.

# Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries

The first trick is the use of the zip() function that will aggregate elements from each provided argument. We pass in the dataset to the *zip()* function with the * operator that separates the dataset (that is a list of lists) into separate lists for each row. The *zip()* function then iterates over each element of each row and returns a column from the dataset as a list of numbers. A clever little trick.

We then calculate the mean, standard deviation and count of rows in each column. A tuple is created from these 3 numbers and a list of these tuples is stored. We then remove the statistics for the class variable as we will not need these statistics.

Let’s test all of these functions on our contrived dataset from above. Below is the complete example.

# Example of summarizing a dataset from math import sqrt # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers)) # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance) # Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries # Test summarizing a dataset dataset = [[3.393533211,2.331273381,0], [3.110073483,1.781539638,0], [1.343808831,3.368360954,0], [3.582294042,4.67917911,0], [2.280362439,2.866990263,0], [7.423436942,4.696522875,1], [5.745051997,3.533989803,1], [9.172168622,2.511101045,1], [7.792783481,3.424088941,1], [7.939820817,0.791637231,1]] summary = summarize_dataset(dataset) print(summary)

Running the example prints out the list of tuples of statistics on each of the two input variables.

Interpreting the results, we can see that the mean value of X1 is 5.178333386499999 and the standard deviation of X1 is 2.7665845055177263.

[(5.178333386499999, 2.7665845055177263, 10), (2.9984683241, 1.218556343617447, 10)]

Now we are ready to use these functions on each group of rows in our dataset.

We require statistics from our training dataset organized by class.

Above, we have developed the *separate_by_class()* function to separate a dataset into rows by class. And we have developed *summarize_dataset()* function to calculate summary statistics for each column.

We can put all of this together and summarize the columns in the dataset organized by class values.

Below is a function named *summarize_by_class()* that implements this operation. The dataset is first split by class, then statistics are calculated on each subset. The results in the form of a list of tuples of statistics are then stored in a dictionary by their class value.

# Split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries

Again, let’s test out all of these behaviors on our contrived dataset.

# Example of summarizing data by class value from math import sqrt # Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers)) # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance) # Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries # Split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries # Test summarizing by class dataset = [[3.393533211,2.331273381,0], [3.110073483,1.781539638,0], [1.343808831,3.368360954,0], [3.582294042,4.67917911,0], [2.280362439,2.866990263,0], [7.423436942,4.696522875,1], [5.745051997,3.533989803,1], [9.172168622,2.511101045,1], [7.792783481,3.424088941,1], [7.939820817,0.791637231,1]] summary = summarize_by_class(dataset) for label in summary: print(label) for row in summary[label]: print(row)

Running this example calculates the statistics for each input variable and prints them organized by class value. Interpreting the results, we can see that the X1 values for rows for class 0 have a mean value of 2.7420144012.

0 (2.7420144012, 0.9265683289298018, 5) (3.0054686692, 1.1073295894898725, 5) 1 (7.6146523718, 1.2344321550313704, 5) (2.9914679790000003, 1.4541931384601618, 5)

There is one more piece we need before we start calculating probabilities.

Calculating the probability or likelihood of observing a given real-value like X1 is difficult.

One way we can do this is to assume that X1 values are drawn from a distribution, such as a bell curve or Gaussian distribution.

A Gaussian distribution can be summarized using only two numbers: the mean and the standard deviation. Therefore, with a little math, we can estimate the probability of a given value. This piece of math is called a Gaussian Probability Distribution Function (or Gaussian PDF) and can be calculated as:

- f(x) = (1 / sqrt(2 * PI) * sigma) * exp(-((x-mean)^2 / (2 * sigma^2)))

Where *sigma* is the standard deviation for *x*, *mean* is the mean for *x* and *PI* is the value of pi.

Below is a function that implements this. I tried to split it up to make it more readable.

# Calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): exponent = exp(-((x-mean)**2 / (2 * stdev**2 ))) return (1 / (sqrt(2 * pi) * stdev)) * exponent

Let’s test it out to see how it works. Below are some worked examples.

# Example of Gaussian PDF from math import sqrt from math import pi from math import exp # Calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): exponent = exp(-((x-mean)**2 / (2 * stdev**2 ))) return (1 / (sqrt(2 * pi) * stdev)) * exponent # Test Gaussian PDF print(calculate_probability(1.0, 1.0, 1.0)) print(calculate_probability(2.0, 1.0, 1.0)) print(calculate_probability(0.0, 1.0, 1.0))

Running it prints the probability of some input values. You can see that when the value is 1 and the mean and standard deviation is 1 our input is the most likely (top of the bell curve) and has the probability of 0.39.

We can see that when we keep the statistics the same and change the x value to 1 standard deviation either side of the mean value (2 and 0 or the same distance either side of the bell curve) the probabilities of those input values are the same at 0.24.

0.3989422804014327 0.24197072451914337 0.24197072451914337

Now that we have all the pieces in place, let’s see how we can calculate the probabilities we need for the Naive Bayes classifier.

Now it is time to use the statistics calculated from our training data to calculate probabilities for new data.

Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate probabilities that it belongs to the second class, and so on for all the classes.

The probability that a piece of data belongs to a class is calculated as follows:

- P(class|data) = P(X|class) * P(class)

You may note that this is different from the Bayes Theorem described above.

The division has been removed to simplify the calculation.

This means that the result is no longer strictly a probability of the data belonging to a class. The value is still maximized, meaning that the calculation for the class that results in the largest value is taken as the prediction. This is a common implementation simplification as we are often more interested in the class prediction rather than the probability.

The input variables are treated separately, giving the technique it’s name “*naive*“. For the above example where we have 2 input variables, the calculation of the probability that a row belongs to the first class 0 can be calculated as:

- P(class=0|X1,X2) = P(X1|class=0) * P(X2|class=0) * P(class=0)

Now you can see why we need to separate the data by class value. The Gaussian Probability Density function in the previous step is how we calculate the probability of a real value like X1 and the statistics we prepared are used in this calculation.

Below is a function named *calculate_class_probabilities()* that ties all of this together.

It takes a set of prepared summaries and a new row as input arguments.

First the total number of training records is calculated from the counts stored in the summary statistics. This is used in the calculation of the probability of a given class or *P(class)* as the ratio of rows with a given class of all rows in the training data.

Next, probabilities are calculated for each input value in the row using the Gaussian probability density function and the statistics for that column and of that class. Probabilities are multiplied together as they accumulated.

This process is repeated for each class in the dataset.

Finally a dictionary of probabilities is returned with one entry for each class.

# Calculate the probabilities of predicting each class for a given row def calculate_class_probabilities(summaries, row): total_rows = sum([summaries[label][0][2] for label in summaries]) probabilities = dict() for class_value, class_summaries in summaries.items(): probabilities[class_value] = summaries[class_value][0][2]/float(total_rows) for i in range(len(class_summaries)): mean, stdev, count = class_summaries[i] probabilities[class_value] *= calculate_probability(row[i], mean, stdev) return probabilities

Let’s tie this together with an example on the contrived dataset.

The example below first calculates the summary statistics by class for the training dataset, then uses these statistics to calculate the probability of the first record belonging to each class.

# Example of calculating class probabilities from math import sqrt from math import pi from math import exp # Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers)) # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance) # Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries # Split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries # Calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): exponent = exp(-((x-mean)**2 / (2 * stdev**2 ))) return (1 / (sqrt(2 * pi) * stdev)) * exponent # Calculate the probabilities of predicting each class for a given row def calculate_class_probabilities(summaries, row): total_rows = sum([summaries[label][0][2] for label in summaries]) probabilities = dict() for class_value, class_summaries in summaries.items(): probabilities[class_value] = summaries[class_value][0][2]/float(total_rows) for i in range(len(class_summaries)): mean, stdev, _ = class_summaries[i] probabilities[class_value] *= calculate_probability(row[i], mean, stdev) return probabilities # Test calculating class probabilities dataset = [[3.393533211,2.331273381,0], [3.110073483,1.781539638,0], [1.343808831,3.368360954,0], [3.582294042,4.67917911,0], [2.280362439,2.866990263,0], [7.423436942,4.696522875,1], [5.745051997,3.533989803,1], [9.172168622,2.511101045,1], [7.792783481,3.424088941,1], [7.939820817,0.791637231,1]] summaries = summarize_by_class(dataset) probabilities = calculate_class_probabilities(summaries, dataset[0]) print(probabilities)

Running the example prints the probabilities calculated for each class.

We can see that the probability of the first row belonging to the 0 class (0.0503) is higher than the probability of it belonging to the 1 class (0.0001). We would therefore correctly conclude that it belongs to the 0 class.

{0: 0.05032427673372075, 1: 0.00011557718379945765}

Now that we have seen how to implement the Naive Bayes algorithm, let’s apply it to the Iris flowers dataset.

This section applies the Naive Bayes algorithm to the Iris flowers dataset.

The first step is to load the dataset and convert the loaded data to numbers that we can use with the mean and standard deviation calculations. For this we will use the helper function *load_csv()* to load the file, *str_column_to_float()* to convert string numbers to floats and *str_column_to_int()* to convert the class column to integer values.

We will evaluate the algorithm using k-fold cross-validation with 5 folds. This means that 150/5=30 records will be in each fold. We will use the helper functions *evaluate_algorithm()* to evaluate the algorithm with cross-validation and *accuracy_metric()* to calculate the accuracy of predictions.

A new function named *predict()* was developed to manage the calculation of the probabilities of a new row belonging to each class and selecting the class with the largest probability value.

Another new function named *naive_bayes()* was developed to manage the application of the Naive Bayes algorithm, first learning the statistics from a training dataset and using them to make predictions for a test dataset.

If you would like more help with the data loading functions used below, see the tutorial:

If you would like more help with the way the model is evaluated using cross validation, see the tutorial:

The complete example is listed below.

# Naive Bayes On The Iris Dataset from csv import reader from random import seed from random import randrange from math import sqrt from math import exp from math import pi # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for _ in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers)) # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance) # Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries # Split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries # Calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): exponent = exp(-((x-mean)**2 / (2 * stdev**2 ))) return (1 / (sqrt(2 * pi) * stdev)) * exponent # Calculate the probabilities of predicting each class for a given row def calculate_class_probabilities(summaries, row): total_rows = sum([summaries[label][0][2] for label in summaries]) probabilities = dict() for class_value, class_summaries in summaries.items(): probabilities[class_value] = summaries[class_value][0][2]/float(total_rows) for i in range(len(class_summaries)): mean, stdev, _ = class_summaries[i] probabilities[class_value] *= calculate_probability(row[i], mean, stdev) return probabilities # Predict the class for a given row def predict(summaries, row): probabilities = calculate_class_probabilities(summaries, row) best_label, best_prob = None, -1 for class_value, probability in probabilities.items(): if best_label is None or probability > best_prob: best_prob = probability best_label = class_value return best_label # Naive Bayes Algorithm def naive_bayes(train, test): summarize = summarize_by_class(train) predictions = list() for row in test: output = predict(summarize, row) predictions.append(output) return(predictions) # Test Naive Bayes on Iris Dataset seed(1) filename = 'iris.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 5 scores = evaluate_algorithm(dataset, naive_bayes, n_folds) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

Running the example prints the mean classification accuracy scores on each cross-validation fold as well as the mean accuracy score.

We can see that the mean accuracy of about 95% is dramatically better than the baseline accuracy of 33%.

Scores: [93.33333333333333, 96.66666666666667, 100.0, 93.33333333333333, 93.33333333333333] Mean Accuracy: 95.333%

We can fit the model on the entire dataset and then use the model to make predictions for new observations (rows of data).

For example, the model is just a set of probabilities calculated via the *summarize_by_class()* function.

... # fit model model = summarize_by_class(dataset)

Once calculated, we can use them in a call to the predict() function with a row representing our new observation to predict the class label.

... # predict the label label = predict(model, row)

We also might like to know the class label (string) for a prediction. We can update the str_column_to_int() function to print the mapping of string class names to integers so we can interpret the prediction by the model.

# Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i print('[%s] => %d' % (value, i)) for row in dataset: row[column] = lookup[row[column]] return lookup

Tying this together, a complete example of fitting the Naive Bayes model on the entire dataset and making a single prediction for a new observation is listed below.

# Make Predictions with Naive Bayes On The Iris Dataset from csv import reader from math import sqrt from math import exp from math import pi # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i print('[%s] => %d' % (value, i)) for row in dataset: row[column] = lookup[row[column]] return lookup # Split the dataset by class values, returns a dictionary def separate_by_class(dataset): separated = dict() for i in range(len(dataset)): vector = dataset[i] class_value = vector[-1] if (class_value not in separated): separated[class_value] = list() separated[class_value].append(vector) return separated # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers)/float(len(numbers)) # Calculate the standard deviation of a list of numbers def stdev(numbers): avg = mean(numbers) variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1) return sqrt(variance) # Calculate the mean, stdev and count for each column in a dataset def summarize_dataset(dataset): summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)] del(summaries[-1]) return summaries # Split dataset by class then calculate statistics for each row def summarize_by_class(dataset): separated = separate_by_class(dataset) summaries = dict() for class_value, rows in separated.items(): summaries[class_value] = summarize_dataset(rows) return summaries # Calculate the Gaussian probability distribution function for x def calculate_probability(x, mean, stdev): exponent = exp(-((x-mean)**2 / (2 * stdev**2 ))) return (1 / (sqrt(2 * pi) * stdev)) * exponent # Calculate the probabilities of predicting each class for a given row def calculate_class_probabilities(summaries, row): total_rows = sum([summaries[label][0][2] for label in summaries]) probabilities = dict() for class_value, class_summaries in summaries.items(): probabilities[class_value] = summaries[class_value][0][2]/float(total_rows) for i in range(len(class_summaries)): mean, stdev, _ = class_summaries[i] probabilities[class_value] *= calculate_probability(row[i], mean, stdev) return probabilities # Predict the class for a given row def predict(summaries, row): probabilities = calculate_class_probabilities(summaries, row) best_label, best_prob = None, -1 for class_value, probability in probabilities.items(): if best_label is None or probability > best_prob: best_prob = probability best_label = class_value return best_label # Make a prediction with Naive Bayes on Iris Dataset filename = 'iris.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # fit model model = summarize_by_class(dataset) # define a new record row = [5.7,2.9,4.2,1.3] # predict the label label = predict(model, row) print('Data=%s, Predicted: %s' % (row, label))

Running the data first summarizes the mapping of class labels to integers and then fits the model on the entire dataset.

Then a new observation is defined (in this case I took a row from the dataset), and a predicted label is calculated. In this case our observation is predicted as belonging to class 1 which we know is “*Iris-versicolor*“.

[Iris-virginica] => 0 [Iris-versicolor] => 1 [Iris-setosa] => 2 Data=[5.7, 2.9, 4.2, 1.3], Predicted: 1

This section lists extensions to the tutorial that you may wish to explore.

**Log Probabilities**: The conditional probabilities for each class given an attribute value are small. When they are multiplied together they result in very small values, which can lead to floating point underflow (numbers too small to represent in Python). A common fix for this is to add the log of the probabilities together. Research and implement this improvement.**Nominal Attributes**: Update the implementation to support nominal attributes. This is much similar and the summary information you can collect for each attribute is the ratio of category values for each class. Dive into the references for more information.**Different Density Function**(*bernoulli*or*multinomial*): We have looked at Gaussian Naive Bayes, but you can also look at other distributions. Implement a different distribution such as multinomial, bernoulli or kernel naive bayes that make different assumptions about the distribution of attribute values and/or their relationship with the class value.

If you try any of these extensions, let me know in the comments below.

- A Gentle Introduction to Bayes Theorem for Machine Learning
- How to Develop a Naive Bayes Classifier from Scratch in Python
- Naive Bayes Tutorial for Machine Learning
- Naive Bayes for Machine Learning
- Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

- Section 13.6 Naive Bayes, page 353, Applied Predictive Modeling, 2013.
- Section 4.2, Statistical modeling, page 88, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition, 2005.

In this tutorial you discovered how to implement the Naive Bayes algorithm from scratch in Python.

Specifically, you learned:

- How to calculate the probabilities required by the Naive interpretation of Bayes Theorem.
- How to use probabilities to make predictions on new data.
- How to apply Naive Bayes to a real-world predictive modeling problem.

Take action!

- Follow the tutorial and implement Naive Bayes from scratch.
- Adapt the example to another dataset.
- Follow the extensions and improve upon the implementation.

Leave a comment and share your experiences.

The post Naive Bayes Classifier From Scratch in Python appeared first on Machine Learning Mastery.

]]>The post What is a Confusion Matrix in Machine Learning appeared first on Machine Learning Mastery.

]]>A confusion matrix is a technique for summarizing the performance of a classification algorithm.

Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your dataset.

Calculating a confusion matrix can give you a better idea of what your classification model is getting right and what types of errors it is making.

In this post, you will discover the confusion matrix for use in machine learning.

After reading this post you will know:

- What the confusion matrix is and why you need to use it.
- How to calculate a confusion matrix for a 2-class classification problem from scratch.
- How create a confusion matrix in Weka, Python and R.

Discover how to code ML algorithms from scratch including kNN, decision trees, neural nets, ensembles and much more in my new book, with full Python code and no fancy libraries.

Let’s get started.

**Update Oct/2017**: Fixed a small bug in the worked example (thanks Raktim).**Update Dec/2017**: Fixed a small bug in accuracy calculation (thanks Robson Pastor Alexandre)

Classification accuracy is the ratio of correct predictions to total predictions made.

classification accuracy = correct predictions / total predictions

It is often presented as a percentage by multiplying the result by 100.

classification accuracy = correct predictions / total predictions * 100

Classification accuracy can also easily be turned into a misclassification rate or error rate by inverting the value, such as:

error rate = (1 - (correct predictions / total predictions)) * 100

Classification accuracy is a great place to start, but often encounters problems in practice.

The main problem with classification accuracy is that it hides the detail you need to better understand the performance of your classification model. There are two examples where you are most likely to encounter this problem:

- When your data has more than 2 classes. With 3 or more classes you may get a classification accuracy of 80%, but you don’t know if that is because all classes are being predicted equally well or whether one or two classes are being neglected by the model.
- When your data does not have an even number of classes. You may achieve accuracy of 90% or more, but this is not a good score if 90 records for every 100 belong to one class and you can achieve this score by always predicting the most common class value.

Classification accuracy can hide the detail you need to diagnose the performance of your model. But thankfully we can tease apart this detail by using a confusion matrix.

A confusion matrix is a summary of prediction results on a classification problem.

The number of correct and incorrect predictions are summarized with count values and broken down by each class. This is the key to the confusion matrix.

**The confusion matrix shows the ways in which your classification model
is confused when it makes predictions.**

It gives you insight not only into the errors being made by your classifier but more importantly the types of errors that are being made.

It is this breakdown that overcomes the limitation of using classification accuracy alone.

Below is the process for calculating a confusion Matrix.

- You need a test dataset or a validation dataset with expected outcome values.
- Make a prediction for each row in your test dataset.
- From the expected outcomes and predictions count:
- The number of correct predictions for each class.
- The number of incorrect predictions for each class, organized by the class that was predicted.

These numbers are then organized into a table, or a matrix as follows:

**Expected down the side**: Each row of the matrix corresponds to a predicted class.**Predicted across the top**: Each column of the matrix corresponds to an actual class.

The counts of correct and incorrect classification are then filled into the table.

The total number of correct predictions for a class go into the expected row for that class value and the predicted column for that class value.

In the same way, the total number of incorrect predictions for a class go into the expected row for that class value and the predicted column for that class value.

In practice, a binary classifier such as this one can make two types of errors: it can incorrectly assign an individual who defaults to the no default category, or it can incorrectly assign an individual who does not default to the default category. It is often of interest to determine which of these two types of errors are being made. A confusion matrix […] is a convenient way to display this information.

— Page 145, An Introduction to Statistical Learning: with Applications in R, 2014

This matrix can be used for 2-class problems where it is very easy to understand, but can easily be applied to problems with 3 or more class values, by adding more rows and columns to the confusion matrix.

Let’s make this explanation of creating a confusion matrix concrete with an example.

Let’s pretend we have a two-class classification problem of predicting whether a photograph contains a man or a woman.

We have a test dataset of 10 records with expected outcomes and a set of predictions from our classification algorithm.

Expected, Predicted man, woman man, man woman, woman man, man woman, man woman, woman woman, woman man, man man, woman woman, woman

Let’s start off and calculate the classification accuracy for this set of predictions.

The algorithm made 7 of the 10 predictions correct with an accuracy of 70%.

accuracy = total correct predictions / total predictions made * 100 accuracy = 7 / 10 * 100

But what type of errors were made?

Let’s turn our results into a confusion matrix.

First, we must calculate the number of correct predictions for each class.

men classified as men: 3 women classified as women: 4

Now, we can calculate the number of incorrect predictions for each class, organized by the predicted value.

men classified as women: 2 woman classified as men: 1

We can now arrange these values into the 2-class confusion matrix:

men women men 3 1 women 2 4

We can learn a lot from this table.

- The total actual men in the dataset is the sum of the values on the men column (3 + 2)
- The total actual women in the dataset is the sum of values in the women column (1 +4).
- The correct values are organized in a diagonal line from top left to bottom-right of the matrix (3 + 4).
- More errors were made by predicting men as women than predicting women as men.

In a two-class problem, we are often looking to discriminate between observations with a specific outcome, from normal observations.

Such as a disease state or event from no disease state or no event.

In this way, we can assign the event row as “*positive*” and the no-event row as “*negative*“. We can then assign the event column of predictions as “*true*” and the no-event as “*false*“.

This gives us:

- “
**true positive**” for correctly predicted event values. - “
**false positive**” for incorrectly predicted event values. - “
**true negative**” for correctly predicted no-event values. - “
**false negative**” for incorrectly predicted no-event values.

We can summarize this in the confusion matrix as follows:

event no-event event true positive false positive no-event false negative true negative

This can help in calculating more advanced classification metrics such as precision, recall, specificity and sensitivity of our classifier.

For example, classification accuracy is calculated as true positives + true negatives.

Consider the case where there are two classes. […] The top row of the table corresponds to samples predicted to be events. Some are predicted correctly (the true positives, or TP) while others are inaccurately classified (false positives or FP). Similarly, the second row contains the predicted negatives with true negatives (TN) and false negatives (FN).

— Page 256, Applied Predictive Modeling, 2013

Now that we have worked through a simple 2-class confusion matrix case study, let’s see how we might calculate a confusion matrix in modern machine learning tools.

This section provides some example of confusion matrices using top machine learning platforms.

These examples will give you a context for what you have learned about the confusion matrix for when you use them in practice with real data and tools.

The Weka machine learning workbench will display a confusion matrix automatically when estimating the skill of a model in the Explorer interface.

Below is a screenshot from the Weka Explorer interface after training a k-nearest neighbor algorithm on the Pima Indians Diabetes dataset.

The confusion matrix is listed at the bottom, and you can see that a wealth of classification statistics are also presented.

The confusion matrix assigns letters a and b to the class values and provides expected class values in rows and predicted class values (“classified as”) for each column.

You can learn more about the Weka Machine Learning Workbench here.

The scikit-learn library for machine learning in Python can calculate a confusion matrix.

Given an array or list of expected values and a list of predictions from your machine learning model, the confusion_matrix() function will calculate a confusion matrix and return the result as an array. You can then print this array and interpret the results.

# Example of a confusion matrix in Python from sklearn.metrics import confusion_matrix expected = [1, 1, 0, 1, 0, 0, 1, 0, 0, 0] predicted = [1, 0, 0, 1, 0, 0, 1, 1, 1, 0] results = confusion_matrix(expected, predicted) print(results)

Running this example prints the confusion matrix array summarizing the results for the contrived 2 class problem.

[[4 2] [1 3]]

Learn more about the confusion_matrix() function in the scikit-learn API documentation.

The caret library for machine learning in R can calculate a confusion matrix.

Given a list of expected values and a list of predictions from your machine learning model, the confusionMatrix() function will calculate a confusion matrix and return the result as a detailed report. You can then print this report and interpret the results.

# example of a confusion matrix in R library(caret) expected <- factor(c(1, 1, 0, 1, 0, 0, 1, 0, 0, 0)) predicted <- factor(c(1, 0, 0, 1, 0, 0, 1, 1, 1, 0)) results <- confusionMatrix(data=predicted, reference=expected) print(results)

Running this example calculates a confusion matrix report and related statistics and prints the results.

Confusion Matrix and Statistics Reference Prediction 0 1 0 4 1 1 2 3 Accuracy : 0.7 95% CI : (0.3475, 0.9333) No Information Rate : 0.6 P-Value [Acc > NIR] : 0.3823 Kappa : 0.4 Mcnemar's Test P-Value : 1.0000 Sensitivity : 0.6667 Specificity : 0.7500 Pos Pred Value : 0.8000 Neg Pred Value : 0.6000 Prevalence : 0.6000 Detection Rate : 0.4000 Detection Prevalence : 0.5000 Balanced Accuracy : 0.7083 'Positive' Class : 0

There is a wealth of information in this report, not least the confusion matrix itself.

Learn more about the confusionMatrix() function in the caret API documentation [PDF].

There is not a lot written about the confusion matrix, but this section lists some additional resources that you may be interested in reading.

- Confusion matrix on Wikipedia
- Simple guide to confusion matrix terminology
- Confusion matrix online calculator

In this post, you discovered the confusion matrix for machine learning.

Specifically, you learned about:

- The limitations of classification accuracy and when it can hide important details.
- The confusion matrix and how to calculate it from scratch and interpret the results.
- How to calculate a confusion matrix with the Weka, Python scikit-learn and R caret libraries.

**Do you have any questions?**

Ask your question in the comments below and I will do my best to answer them.

The post What is a Confusion Matrix in Machine Learning appeared first on Machine Learning Mastery.

]]>The post How to Implement Stacked Generalization (Stacking) From Scratch With Python appeared first on Machine Learning Mastery.

]]>Ensemble methods are an excellent way to improve predictive performance on your machine learning problems.

Stacked Generalization or stacking is an ensemble technique that uses a new model to learn how to best combine the predictions from two or more models trained on your dataset.

In this tutorial, you will discover how to implement stacking from scratch in Python.

After completing this tutorial, you will know:

- How to learn to combine the predictions from multiple models on a dataset.
- How to apply stacked generalization to a real-world predictive modeling problem.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief overview of the Stacked Generalization algorithm and the Sonar dataset used in this tutorial.

Stacked Generalization or stacking is an ensemble algorithm where a new model is trained to combine the predictions from two or more models already trained or your dataset.

The predictions from the existing models or submodels are combined using a new model, and as such stacking is often referred to as blending, as the predictions from sub-models are blended together.

It is typical to use a simple linear method to combine the predictions for submodels such as simple averaging or voting, to a weighted sum using linear regression or logistic regression.

Models that have their predictions combined must have skill on the problem, but do not need to be the best possible models. This means that you do not need to tune the submodels intently, as long as the model shows some advantage over a baseline prediction.

It is important that sub-models produce different predictions, so-called uncorrelated predictions. Stacking works best when the predictions that are combined are all skillful, but skillful in different ways. This may be achieved by using algorithms that use very different internal representations (trees compared to instances) and/or models trained on different representations or projections of the training data.

In this tutorial, we will look at taking two very different and untuned sub-models and combining their predictions with a simple logistic regression algorithm.

The dataset we will use in this tutorial is the Sonar dataset.

This is a dataset that describes sonar chirp returns bouncing off different surfaces. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders. There are 208 observations.

It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.

By predicting the class with the most observations in the dataset (M or mines) the Zero Rule Algorithm can achieve an accuracy of about 53%.

You can learn more about this dataset at the UCI Machine Learning repository.

Download the dataset for free and place it in your working directory with the filename **sonar.all-data.csv**.

This tutorial is broken down into 3 steps:

- Sub-models and Aggregator.
- Combining Predictions.
- Sonar Dataset Case Study.

These steps provide the foundation that you need to understand and implement stacking on your own predictive modeling problems.

We are going to use two models as submodels for stacking and a linear model as the aggregator model.

This part is divided into 3 sections:

- Sub-model #1: k-Nearest Neighbors.
- Sub-model #2: Perceptron.
- Aggregator Model: Logistic Regression.

Each model will be described in terms of the functions used train the model and a function used to make predictions.

The k-Nearest Neighbors algorithm or kNN uses the entire training dataset as the model.

Therefore training the model involves retaining the training dataset. Below is a function named **knn_model()** that does just this.

# Prepare the kNN model def knn_model(train): return train

Making predictions involves finding the k most similar records in the training dataset and selecting the most common class values. The Euclidean distance function is used to calculate the similarity between new rows of data and rows in the training dataset.

Below are these helper functions that involve making predictions for a kNN model. The function **euclidean_distance()** calculates the distance between two rows of data, **get_neighbors()** locates all neighbors for in the training dataset for a new row of data and **knn_predict()** makes a prediction from the neighbors for a new row of data.

# Calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate neighbors for a new row def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Make a prediction with kNN def knn_predict(model, test_row, num_neighbors=2): neighbors = get_neighbors(model, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction

You can see that the number of neighbors (k) is set to 2 as a default parameter on the **knn_predict()** function. This number was chosen with a little trial and error and was not tuned.

Now that we have the building blocks for a kNN model, let’s look at the Perceptron algorithm.

The model for the Perceptron algorithm is a set of weights learned from the training data.

In order to train the weights, many predictions need to be made on the training data in order to calculate error values. Therefore, both model training and prediction require a function for prediction.

Below are the helper functions for implementing the Perceptron algorithm. The **perceptron_model()** function trains the Perceptron model on the training dataset and **perceptron_predict()** is used to make a prediction for a row of data.

# Make a prediction with weights def perceptron_predict(model, row): activation = model[0] for i in range(len(row)-1): activation += model[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # Estimate Perceptron weights using stochastic gradient descent def perceptron_model(train, l_rate=0.01, n_epoch=5000): weights = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): for row in train: prediction = perceptron_predict(weights, row) error = row[-1] - prediction weights[0] = weights[0] + l_rate * error for i in range(len(row)-1): weights[i + 1] = weights[i + 1] + l_rate * error * row[i] return weights

The **perceptron_model()** model specifies both a learning rate and number of training epochs as default parameters. Again, these parameters were chosen with a little bit of trial and error, but were not tuned on the dataset.

We now have implementations for both sub-models, let’s look at implementing the aggregator model.

Like the Perceptron algorithm, Logistic Regression uses a set of weights, called coefficients, as the representation of the model.

And like the Perceptron algorithm, the coefficients are learned by iteratively making predictions on the training data and updating them.

Below are the helper functions for implementing the logistic regression algorithm. The **logistic_regression_model()** function is used to train the coefficients on the training dataset and **logistic_regression_predict()** is used to make a prediction for a row of data.

# Make a prediction with coefficients def logistic_regression_predict(model, row): yhat = model[0] for i in range(len(row)-1): yhat += model[i + 1] * row[i] return 1.0 / (1.0 + exp(-yhat)) # Estimate logistic regression coefficients using stochastic gradient descent def logistic_regression_model(train, l_rate=0.01, n_epoch=5000): coef = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): for row in train: yhat = logistic_regression_predict(coef, row) error = row[-1] - yhat coef[0] = coef[0] + l_rate * error * yhat * (1.0 - yhat) for i in range(len(row)-1): coef[i + 1] = coef[i + 1] + l_rate * error * yhat * (1.0 - yhat) * row[i] return coef

The **logistic_regression_model()** defines a learning rate and number of epochs as default parameters, and as with the other algorithms, these parameters were found with a little trial and error and were not optimized.

Now that we have implementations of sub-models and the aggregator model, let’s see how we can combine the predictions from multiple models.

For a machine learning algorithm, learning how to combine predictions is much the same as learning from a training dataset.

A new training dataset can be constructed from the predictions of the sub-models, as follows:

- Each row represents one row in the training dataset.
- The first column contains predictions for each row in the training dataset made by the first sub-model, such as k-Nearest Neighbors.
- The second column contains predictions for each row in the training dataset made by the second sub-model, such as the Perceptron algorithm.
- The third column contains the expected output value for the row in the training dataset.

Below is a contrived example of what a constructed stacking dataset may look like:

kNN, Per, Y 0, 0 0 1, 0 1 0, 1 0 1, 1 1 0, 1 0

A machine learning algorithm, such as logistic regression can then be trained on this new dataset. In essence, this new meta-algorithm learns how to best combine the prediction from multiple submodels.

Below is a function named **to_stacked_row()** that implements this procedure for creating new rows for this stacked dataset.

The function takes a list of models as input, these are used to make predictions. The function also takes a list of functions as input, one function used to make a prediction for each model. Finally, a single row from the training dataset is included.

A new row is constructed one column at a time. Predictions are calculated using each model and the row of training data. The expected output value from the training dataset row is then added as the last column to the row.

# Make predictions with sub-models and construct a new stacked row def to_stacked_row(models, predict_list, row): stacked_row = list() for i in range(len(models)): prediction = predict_list[i](models[i], row) stacked_row.append(prediction) stacked_row.append(row[-1]) return stacked_row

On some predictive modeling problems, it is possible to get an even larger boost by training the aggregated model on both the training row and the predictions made by sub-models.

This improvement gives the aggregator model both the context of all the data in the training row to help determine how and when to best combine the predictions of the sub-models.

We can update our **to_stacked_row()** function to include this by aggregating the training row (minus the final column) and the stacked row as created above.

Below is an updated version of the **to_stacked_row()** function that implements this improvement.

# Make predictions with sub-models and construct a new stacked row def to_stacked_row(models, predict_list, row): stacked_row = list() for i in range(len(models)): prediction = predict_list[i](models[i], row) stacked_row.append(prediction) stacked_row.append(row[-1]) return row[0:len(row)-1] + stacked_row

It is a good idea to try both approaches on your problem to see which works best.

Now that we have all of the pieces for stacked generalization, we can apply it to a real-world problem.

In this section, we will apply the Stacking algorithm to the Sonar dataset.

The example assumes that a CSV copy of the dataset is in the current working directory with the filename **sonar.all-data.csv**.

The dataset is first loaded, the string values converted to numeric and the output column is converted from strings to the integer values of 0 to 1. This is achieved with helper functions **load_csv()**, **str_column_to_float()** and **str_column_to_int()** to load and prepare the dataset.

We will use k-fold cross validation to estimate the performance of the learned model on unseen data. This means that we will construct and evaluate k models and estimate the performance as the mean model error. Classification accuracy will be used to evaluate the model. These behaviors are provided in the **cross_validation_split()**, **accuracy_metric()** and **evaluate_algorithm()** helper functions.

We will use the k-Nearest Neighbors, Perceptron and Logistic Regression algorithms implemented above. We will also use our technique for creating the new stacked dataset defined in the previous step.

A new function name **stacking()** is developed. This function does 4 things:

- It first trains a list of models (kNN and Perceptron).
- It then uses the models to make predictions and create a new stacked dataset.
- It then trains an aggregator model (logistic regression) on the stacked dataset.
- It then uses the sub-models and aggregator model to make predictions on the test dataset.

The complete example is listed below.

# Test stacking on the sonar dataset from random import seed from random import randrange from csv import reader from math import sqrt from math import exp # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate neighbors for a new row def get_neighbors(train, test_row, num_neighbors): distances = list() for train_row in train: dist = euclidean_distance(test_row, train_row) distances.append((train_row, dist)) distances.sort(key=lambda tup: tup[1]) neighbors = list() for i in range(num_neighbors): neighbors.append(distances[i][0]) return neighbors # Make a prediction with kNN def knn_predict(model, test_row, num_neighbors=2): neighbors = get_neighbors(model, test_row, num_neighbors) output_values = [row[-1] for row in neighbors] prediction = max(set(output_values), key=output_values.count) return prediction # Prepare the kNN model def knn_model(train): return train # Make a prediction with weights def perceptron_predict(model, row): activation = model[0] for i in range(len(row)-1): activation += model[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # Estimate Perceptron weights using stochastic gradient descent def perceptron_model(train, l_rate=0.01, n_epoch=5000): weights = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): for row in train: prediction = perceptron_predict(weights, row) error = row[-1] - prediction weights[0] = weights[0] + l_rate * error for i in range(len(row)-1): weights[i + 1] = weights[i + 1] + l_rate * error * row[i] return weights # Make a prediction with coefficients def logistic_regression_predict(model, row): yhat = model[0] for i in range(len(row)-1): yhat += model[i + 1] * row[i] return 1.0 / (1.0 + exp(-yhat)) # Estimate logistic regression coefficients using stochastic gradient descent def logistic_regression_model(train, l_rate=0.01, n_epoch=5000): coef = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): for row in train: yhat = logistic_regression_predict(coef, row) error = row[-1] - yhat coef[0] = coef[0] + l_rate * error * yhat * (1.0 - yhat) for i in range(len(row)-1): coef[i + 1] = coef[i + 1] + l_rate * error * yhat * (1.0 - yhat) * row[i] return coef # Make predictions with sub-models and construct a new stacked row def to_stacked_row(models, predict_list, row): stacked_row = list() for i in range(len(models)): prediction = predict_list[i](models[i], row) stacked_row.append(prediction) stacked_row.append(row[-1]) return row[0:len(row)-1] + stacked_row # Stacked Generalization Algorithm def stacking(train, test): model_list = [knn_model, perceptron_model] predict_list = [knn_predict, perceptron_predict] models = list() for i in range(len(model_list)): model = model_list[i](train) models.append(model) stacked_dataset = list() for row in train: stacked_row = to_stacked_row(models, predict_list, row) stacked_dataset.append(stacked_row) stacked_model = logistic_regression_model(stacked_dataset) predictions = list() for row in test: stacked_row = to_stacked_row(models, predict_list, row) stacked_dataset.append(stacked_row) prediction = logistic_regression_predict(stacked_model, stacked_row) prediction = round(prediction) predictions.append(prediction) return predictions # Test stacking on the sonar dataset seed(1) # load and prepare data filename = 'sonar.all-data.csv' dataset = load_csv(filename) # convert string attributes to integers for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) n_folds = 3 scores = evaluate_algorithm(dataset, stacking, n_folds) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

A k value of 3 was used for cross-validation, giving each fold 208/3 = 69.3 or just under 70 records to be evaluated upon each iteration.

Running the example prints the scores and mean of the scores for the final configuration.

Scores: [78.26086956521739, 76.81159420289855, 69.56521739130434] Mean Accuracy: 74.879%

This section lists extensions to this tutorial that you may be interested in exploring.

**Tune Algorithms**. The algorithms used for the submodels and the aggregate model in this tutorial were not tuned. Explore alternate configurations and see if you can further lift performance.**Prediction Correlations**. Stacking works better if the predictions of submodels are weakly correlated. Implement calculations to estimate the correlation between the predictions of submodels.**Different Sub-models**. Implement more and different sub-models to be combined using the stacking procedure.**Different Aggregating Model**. Experiment with simpler models (like averaging and voting) and more complex aggregation models to see if you can boost performance.**More Datasets**. Apply stacking to more datasets on the UCI Machine Learning Repository.

**Did you explore any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement the stacking algorithm from scratch in Python.

Specifically, you learned:

- How to combine the predictions from multiple models.
- How to apply stacking to a real-world predictive modeling problem.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer.

The post How to Implement Stacked Generalization (Stacking) From Scratch With Python appeared first on Machine Learning Mastery.

]]>The post How to Implement Random Forest From Scratch in Python appeared first on Machine Learning Mastery.

]]>Building multiple models from samples of your training data, called bagging, can reduce this variance, but the trees are highly correlated.

Random Forest is an extension of bagging that in addition to building trees based on multiple samples of your training data, it also constrains the features that can be used to build the trees, forcing trees to be different. This, in turn, can give a lift in performance.

In this tutorial, you will discover how to implement the Random Forest algorithm from scratch in Python.

After completing this tutorial, you will know:

- The difference between bagged decision trees and the random forest algorithm.
- How to construct bagged decision trees with more variance.
- How to apply the random forest algorithm to a predictive modeling problem.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Feb/2017**: Fixed a bug in build_tree.**Update Aug/2017**: Fixed a bug in Gini calculation, added the missing weighting of group Gini scores by group size (thanks Michael!).**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief introduction to the Random Forest algorithm and the Sonar dataset used in this tutorial.

Decision trees involve the greedy selection of the best split point from the dataset at each step.

This algorithm makes decision trees susceptible to high variance if they are not pruned. This high variance can be harnessed and reduced by creating multiple trees with different samples of the training dataset (different views of the problem) and combining their predictions. This approach is called bootstrap aggregation or bagging for short.

A limitation of bagging is that the same greedy algorithm is used to create each tree, meaning that it is likely that the same or very similar split points will be chosen in each tree making the different trees very similar (trees will be correlated). This, in turn, makes their predictions similar, mitigating the variance originally sought.

We can force the decision trees to be different by limiting the features (rows) that the greedy algorithm can evaluate at each split point when creating the tree. This is called the Random Forest algorithm.

Like bagging, multiple samples of the training dataset are taken and a different tree trained on each. The difference is that at each point a split is made in the data and added to the tree, only a fixed subset of attributes can be considered.

For classification problems, the type of problems we will look at in this tutorial, the number of attributes to be considered for the split is limited to the square root of the number of input features.

num_features_for_split = sqrt(total_input_features)

The result of this one small change are trees that are more different from each other (uncorrelated) resulting predictions that are more diverse and a combined prediction that often has better performance that single tree or bagging alone.

The dataset we will use in this tutorial is the Sonar dataset.

This is a dataset that describes sonar chirp returns bouncing off different surfaces. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders. There are 208 observations.

It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.

By predicting the class with the most observations in the dataset (M or mines) the Zero Rule Algorithm can achieve an accuracy of 53%.

You can learn more about this dataset at the UCI Machine Learning repository.

Download the dataset for free and place it in your working directory with the filename **sonar.all-data.csv**.

This tutorial is broken down into 2 steps.

- Calculating Splits.
- Sonar Dataset Case Study.

These steps provide the foundation that you need to implement and apply the Random Forest algorithm to your own predictive modeling problems.

In a decision tree, split points are chosen by finding the attribute and the value of that attribute that results in the lowest cost.

For classification problems, this cost function is often the Gini index, that calculates the purity of the groups of data created by the split point. A Gini index of 0 is perfect purity where class values are perfectly separated into two groups, in the case of a two-class classification problem.

Finding the best split point in a decision tree involves evaluating the cost of each value in the training dataset for each input variable.

For bagging and random forest, this procedure is executed upon a sample of the training dataset, made with replacement. Sampling with replacement means that the same row may be chosen and added to the sample more than once.

We can update this procedure for Random Forest. Instead of enumerating all values for input attributes in search if the split with the lowest cost, we can create a sample of the input attributes to consider.

This sample of input attributes can be chosen randomly and without replacement, meaning that each input attribute needs only be considered once when looking for the split point with the lowest cost.

Below is a function name **get_split()** that implements this procedure. It takes a dataset and a fixed number of input features from to evaluate as input arguments, where the dataset may be a sample of the actual training dataset.

The helper function **test_split()** is used to split the dataset by a candidate split point and **gini_index()** is used to evaluate the cost of a given split by the groups of rows created.

We can see that a list of features is created by randomly selecting feature indices and adding them to a list (called **features**), this list of features is then enumerated and specific values in the training dataset evaluated as split points.

# Select the best split point for a dataset def get_split(dataset, n_features): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None features = list() while len(features) < n_features: index = randrange(len(dataset[0])-1) if index not in features: features.append(index) for index in features: for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups}

Now that we know how a decision tree algorithm can be modified for use with the Random Forest algorithm, we can piece this together with an implementation of bagging and apply it to a real-world dataset.

In this section, we will apply the Random Forest algorithm to the Sonar dataset.

The example assumes that a CSV copy of the dataset is in the current working directory with the file name **sonar.all-data.csv**.

The dataset is first loaded, the string values converted to numeric and the output column is converted from strings to the integer values of 0 and 1. This is achieved with helper functions **load_csv()**, **str_column_to_float()** and **str_column_to_int()** to load and prepare the dataset.

We will use k-fold cross validation to estimate the performance of the learned model on unseen data. This means that we will construct and evaluate k models and estimate the performance as the mean model error. Classification accuracy will be used to evaluate each model. These behaviors are provided in the **cross_validation_split()**, **accuracy_metric()** and **evaluate_algorithm()** helper functions.

We will also use an implementation of the Classification and Regression Trees (CART) algorithm adapted for bagging including the helper functions **test_split()** to split a dataset into groups, **gini_index()** to evaluate a split point, our modified **get_split()** function discussed in the previous step, **to_terminal()**, **split()** and **build_tree()** used to create a single decision tree, **predict()** to make a prediction with a decision tree, **subsample()** to make a subsample of the training dataset and **bagging_predict()** to make a prediction with a list of decision trees.

A new function name **random_forest()** is developed that first creates a list of decision trees from subsamples of the training dataset and then uses them to make predictions.

As we stated above, the key difference between Random Forest and bagged decision trees is the one small change to the way that trees are created, here in the **get_split()** function.

The complete example is listed below.

# Random Forest Algorithm on Sonar Dataset from random import seed from random import randrange from csv import reader from math import sqrt # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right # Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # Select the best split point for a dataset def get_split(dataset, n_features): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None features = list() while len(features) < n_features: index = randrange(len(dataset[0])-1) if index not in features: features.append(index) for index in features: for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups} # Create a terminal node value def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count) # Create child splits for a node or make terminal def split(node, max_depth, min_size, n_features, depth): left, right = node['groups'] del(node['groups']) # check for a no split if not left or not right: node['left'] = node['right'] = to_terminal(left + right) return # check for max depth if depth >= max_depth: node['left'], node['right'] = to_terminal(left), to_terminal(right) return # process left child if len(left) <= min_size: node['left'] = to_terminal(left) else: node['left'] = get_split(left, n_features) split(node['left'], max_depth, min_size, n_features, depth+1) # process right child if len(right) <= min_size: node['right'] = to_terminal(right) else: node['right'] = get_split(right, n_features) split(node['right'], max_depth, min_size, n_features, depth+1) # Build a decision tree def build_tree(train, max_depth, min_size, n_features): root = get_split(train, n_features) split(root, max_depth, min_size, n_features, 1) return root # Make a prediction with a decision tree def predict(node, row): if row[node['index']] < node['value']: if isinstance(node['left'], dict): return predict(node['left'], row) else: return node['left'] else: if isinstance(node['right'], dict): return predict(node['right'], row) else: return node['right'] # Create a random subsample from the dataset with replacement def subsample(dataset, ratio): sample = list() n_sample = round(len(dataset) * ratio) while len(sample) < n_sample: index = randrange(len(dataset)) sample.append(dataset[index]) return sample # Make a prediction with a list of bagged trees def bagging_predict(trees, row): predictions = [predict(tree, row) for tree in trees] return max(set(predictions), key=predictions.count) # Random Forest Algorithm def random_forest(train, test, max_depth, min_size, sample_size, n_trees, n_features): trees = list() for i in range(n_trees): sample = subsample(train, sample_size) tree = build_tree(sample, max_depth, min_size, n_features) trees.append(tree) predictions = [bagging_predict(trees, row) for row in test] return(predictions) # Test the random forest algorithm seed(2) # load and prepare data filename = 'sonar.all-data.csv' dataset = load_csv(filename) # convert string attributes to integers for i in range(0, len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 5 max_depth = 10 min_size = 1 sample_size = 1.0 n_features = int(sqrt(len(dataset[0])-1)) for n_trees in [1, 5, 10]: scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features) print('Trees: %d' % n_trees) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

A k value of 5 was used for cross-validation, giving each fold 208/5 = 41.6 or just over 40 records to be evaluated upon each iteration.

Deep trees were constructed with a max depth of 10 and a minimum number of training rows at each node of 1. Samples of the training dataset were created with the same size as the original dataset, which is a default expectation for the Random Forest algorithm.

The number of features considered at each split point was set to sqrt(num_features) or sqrt(60)=7.74 rounded to 7 features.

A suite of 3 different numbers of trees were evaluated for comparison, showing the increasing skill as more trees are added.

Running the example prints the scores for each fold and mean score for each configuration.

Trees: 1 Scores: [56.09756097560976, 63.41463414634146, 60.97560975609756, 58.536585365853654, 73.17073170731707] Mean Accuracy: 62.439% Trees: 5 Scores: [70.73170731707317, 58.536585365853654, 85.36585365853658, 75.60975609756098, 63.41463414634146] Mean Accuracy: 70.732% Trees: 10 Scores: [75.60975609756098, 80.48780487804879, 92.6829268292683, 73.17073170731707, 70.73170731707317] Mean Accuracy: 78.537%

This section lists extensions to this tutorial that you may be interested in exploring.

**Algorithm Tuning**. The configuration used in the tutorial was found with a little trial and error but was not optimized. Experiment with larger numbers of trees, different numbers of features and even different tree configurations to improve performance.**More Problems**. Apply the technique to other classification problems and even adapt it for regression with a new cost function and a new method for combining the predictions from trees.

**Did you try any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement the Random Forest algorithm from scratch.

Specifically, you learned:

- The difference between Random Forest and Bagged Decision Trees.
- How to update the creation of decision trees to accommodate the Random Forest procedure.
- How to apply the Random Forest algorithm to a real world predictive modeling problem.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer.

The post How to Implement Random Forest From Scratch in Python appeared first on Machine Learning Mastery.

]]>The post How to Implement Bagging From Scratch With Python appeared first on Machine Learning Mastery.

]]>This means that trees can get very different results given different training data.

A technique to make decision trees more robust and to achieve better performance is called bootstrap aggregation or bagging for short.

In this tutorial, you will discover how to implement the bagging procedure with decision trees from scratch with Python.

After completing this tutorial, you will know:

- How to create a bootstrap sample of your dataset.
- How to make predictions with bootstrapped models.
- How to apply bagging to your own predictive modeling problems.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Feb/2017**: Fixed a bug in build_tree.**Update Aug/2017**: Fixed a bug in Gini calculation, added the missing weighting of group Gini scores by group size (thanks Michael!).**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief description to Bootstrap Aggregation and the Sonar dataset that will be used in this tutorial.

A bootstrap is a sample of a dataset with replacement.

This means that a new dataset is created from a random sample of an existing dataset where a given row may be selected and added more than once to the sample.

It is a useful approach to use when estimating values such as the mean for a broader dataset, when you only have a limited dataset available. By creating samples of your dataset and estimating the mean from those samples, you can take the average of those estimates and get a better idea of the true mean of the underlying problem.

This same approach can be used with machine learning algorithms that have a high variance, such as decision trees. A separate model is trained on each bootstrap sample of data and the average output of those models used to make predictions. This technique is called bootstrap aggregation or bagging for short.

Variance means that an algorithm’s performance is sensitive to the training data, with high variance suggesting that the more the training data is changed, the more the performance of the algorithm will vary.

The performance of high variance machine learning algorithms like unpruned decision trees can be improved by training many trees and taking the average of their predictions. Results are often better than a single decision tree.

Another benefit of bagging in addition to improved performance is that the bagged decision trees cannot overfit the problem. Trees can continue to be added until a maximum in performance is achieved.

The dataset we will use in this tutorial is the Sonar dataset.

This is a dataset that describes sonar chirp returns bouncing off different surfaces. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders. There are 208 observations.

It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.

By predicting the class with the most observations in the dataset (M or mines) the Zero Rule Algorithm can achieve an accuracy of 53%.

You can learn more about this dataset at the UCI Machine Learning repository.

Download the dataset for free and place it in your working directory with the filename **sonar.all-data.csv**.

This tutorial is broken down into 2 parts:

- Bootstrap Resample.
- Sonar Dataset Case Study.

These steps provide the foundation that you need to implement and apply bootstrap aggregation with decision trees to your own predictive modeling problems.

Let’s start off by getting a strong idea of how the bootstrap method works.

We can create a new sample of a dataset by randomly selecting rows from the dataset and adding them to a new list. We can repeat this for a fixed number of rows or until the size of the new dataset matches a ratio of the size of the original dataset.

We can allow sampling with replacement by not removing the row that was selected so that it is available for future selections.

Below is a function named **subsample()** that implements this procedure. The **randrange()** function from the random module is used to select a random row index to add to the sample each iteration of the loop. The default size of the sample is the size of the original dataset.

# Create a random subsample from the dataset with replacement def subsample(dataset, ratio=1.0): sample = list() n_sample = round(len(dataset) * ratio) while len(sample) < n_sample: index = randrange(len(dataset)) sample.append(dataset[index]) return sample

We can use this function to estimate the mean of a contrived dataset.

First, we can create a dataset with 20 rows and a single column of random numbers between 0 and 9 and calculate the mean value.

We can then make bootstrap samples of the original dataset, calculate the mean, and repeat this process until we have a list of means. Taking the average of these sample means should give us a robust estimate of the mean of the entire dataset.

The complete example is listed below.

Each bootstrap sample is created as a 10% sample of the original 20 observation dataset (or 2 observations). We then experiment by creating 1, 10, 100 bootstrap samples of the original dataset, calculate their mean value, then average all of those estimated mean values.

from random import seed from random import random from random import randrange # Create a random subsample from the dataset with replacement def subsample(dataset, ratio=1.0): sample = list() n_sample = round(len(dataset) * ratio) while len(sample) < n_sample: index = randrange(len(dataset)) sample.append(dataset[index]) return sample # Calculate the mean of a list of numbers def mean(numbers): return sum(numbers) / float(len(numbers)) seed(1) # True mean dataset = [[randrange(10)] for i in range(20)] print('True Mean: %.3f' % mean([row[0] for row in dataset])) # Estimated means ratio = 0.10 for size in [1, 10, 100]: sample_means = list() for i in range(size): sample = subsample(dataset, ratio) sample_mean = mean([row[0] for row in sample]) sample_means.append(sample_mean) print('Samples=%d, Estimated Mean: %.3f' % (size, mean(sample_means)))

Running the example prints the original mean value we aim to estimate.

We can then see the estimated mean from the various different numbers of bootstrap samples. We can see that with 100 samples we achieve a good estimate of the mean.

True Mean: 4.450 Samples=1, Estimated Mean: 4.500 Samples=10, Estimated Mean: 3.300 Samples=100, Estimated Mean: 4.480

Instead of calculating the mean value, we can create a model from each subsample.

Next, let’s see how we can combine the predictions from multiple bootstrap models.

In this section, we will apply the Random Forest algorithm to the Sonar dataset.

The example assumes that a CSV copy of the dataset is in the current working directory with the file name **sonar.all-data.csv**.

The dataset is first loaded, the string values converted to numeric and the output column is converted from strings to the integer values of 0 to 1. This is achieved with helper functions **load_csv()**, **str_column_to_float()** and **str_column_to_int()** to load and prepare the dataset.

We will use k-fold cross validation to estimate the performance of the learned model on unseen data. This means that we will construct and evaluate k models and estimate the performance as the mean model error. Classification accuracy will be used to evaluate each model. These behaviors are provided in the **cross_validation_split()**, **accuracy_metric()** and **evaluate_algorithm()** helper functions.

We will also use an implementation of the Classification and Regression Trees (CART) algorithm adapted for bagging including the helper functions **test_split()** to split a dataset into groups, **gini_index()** to evaluate a split point, **get_split()** to find an optimal split point, **to_terminal()**, **split()** and **build_tree()** used to create a single decision tree, **predict()** to make a prediction with a decision tree and the **subsample()** function described in the previous step to make a subsample of the training dataset

A new function named **bagging_predict()** is developed that is responsible for making a prediction with each decision tree and combining the predictions into a single return value. This is achieved by selecting the most common prediction from the list of predictions made by the bagged trees.

Finally, a new function named **bagging()** is developed that is responsible for creating the samples of the training dataset, training a decision tree on each, then making predictions on the test dataset using the list of bagged trees.

The complete example is listed below.

# Bagging Algorithm on the Sonar dataset from random import seed from random import randrange from csv import reader # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right # Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # Select the best split point for a dataset def get_split(dataset): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None for index in range(len(dataset[0])-1): for row in dataset: # for i in range(len(dataset)): # row = dataset[randrange(len(dataset))] groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups} # Create a terminal node value def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count) # Create child splits for a node or make terminal def split(node, max_depth, min_size, depth): left, right = node['groups'] del(node['groups']) # check for a no split if not left or not right: node['left'] = node['right'] = to_terminal(left + right) return # check for max depth if depth >= max_depth: node['left'], node['right'] = to_terminal(left), to_terminal(right) return # process left child if len(left) <= min_size: node['left'] = to_terminal(left) else: node['left'] = get_split(left) split(node['left'], max_depth, min_size, depth+1) # process right child if len(right) <= min_size: node['right'] = to_terminal(right) else: node['right'] = get_split(right) split(node['right'], max_depth, min_size, depth+1) # Build a decision tree def build_tree(train, max_depth, min_size): root = get_split(train) split(root, max_depth, min_size, 1) return root # Make a prediction with a decision tree def predict(node, row): if row[node['index']] < node['value']: if isinstance(node['left'], dict): return predict(node['left'], row) else: return node['left'] else: if isinstance(node['right'], dict): return predict(node['right'], row) else: return node['right'] # Create a random subsample from the dataset with replacement def subsample(dataset, ratio): sample = list() n_sample = round(len(dataset) * ratio) while len(sample) < n_sample: index = randrange(len(dataset)) sample.append(dataset[index]) return sample # Make a prediction with a list of bagged trees def bagging_predict(trees, row): predictions = [predict(tree, row) for tree in trees] return max(set(predictions), key=predictions.count) # Bootstrap Aggregation Algorithm def bagging(train, test, max_depth, min_size, sample_size, n_trees): trees = list() for i in range(n_trees): sample = subsample(train, sample_size) tree = build_tree(sample, max_depth, min_size) trees.append(tree) predictions = [bagging_predict(trees, row) for row in test] return(predictions) # Test bagging on the sonar dataset seed(1) # load and prepare data filename = 'sonar.all-data.csv' dataset = load_csv(filename) # convert string attributes to integers for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 5 max_depth = 6 min_size = 2 sample_size = 0.50 for n_trees in [1, 5, 10, 50]: scores = evaluate_algorithm(dataset, bagging, n_folds, max_depth, min_size, sample_size, n_trees) print('Trees: %d' % n_trees) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

A k value of 5 was used for cross-validation, giving each fold 208/5 = 41.6 or just over 40 records to be evaluated upon each iteration.

Deep trees were constructed with a max depth of 6 and a minimum number of training rows at each node of 2. Samples of the training dataset were created with 50% the size of the original dataset. This was to force some variety in the dataset subsamples used to train each tree. The default for bagging is to have the size of sample datasets match the size of the original training dataset.

A series of 4 different numbers of trees were evaluated to show the behavior of the algorithm.

The accuracy from each fold and the mean accuracy for each configuration are printed. We can see a trend of some minor lift in performance as the number of trees is increased.

Trees: 1 Scores: [87.8048780487805, 65.85365853658537, 65.85365853658537, 65.85365853658537, 73.17073170731707] Mean Accuracy: 71.707% Trees: 5 Scores: [60.97560975609756, 80.48780487804879, 78.04878048780488, 82.92682926829268, 63.41463414634146] Mean Accuracy: 73.171% Trees: 10 Scores: [60.97560975609756, 73.17073170731707, 82.92682926829268, 80.48780487804879, 68.29268292682927] Mean Accuracy: 73.171% Trees: 50 Scores: [63.41463414634146, 75.60975609756098, 80.48780487804879, 75.60975609756098, 85.36585365853658] Mean Accuracy: 76.098%

A difficulty of this method is that even though deep trees are constructed, the bagged trees that are created are very similar. In turn, the predictions made by these trees are also similar, and the high variance we desire among the trees trained on different samples of the training dataset is diminished.

This is because of the greedy algorithm used in the construction of the trees selecting the same or similar split points.

The tutorial tried to re-inject this variance by constraining the sample size used to train each tree. A more robust technique is to constrain the features that may be evaluated when creating each split point. This is the method used in the Random Forest algorithm.

**Tune the Example**. Explore different configurations for the number of trees and even individual tree configurations to see if you can further improve results.**Bag Another Algorithm**. Other algorithms can be used with bagging. For example, a k-nearest neighbor algorithm with a low value of k will have a high variance and is a good candidate for bagging.**Regression Problems**. Bagging can be used with regression trees. Instead of predicting the most common class value from the set of predictions, you can return the average of the predictions from the bagged trees. Experiment on regression problems.

**Did you try any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement bootstrap aggregation from scratch with Python.

Specifically, you learned:

- How to create a subsample and estimate bootstrap quantities.
- How to create an ensemble of decision trees and use them to make predictions.
- How to apply bagging to a real world predictive modeling problem.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer.

The post How to Implement Bagging From Scratch With Python appeared first on Machine Learning Mastery.

]]>The post How To Implement The Decision Tree Algorithm From Scratch In Python appeared first on Machine Learning Mastery.

]]>They are popular because the final model is so easy to understand by practitioners and domain experts alike. The final decision tree can explain exactly why a specific prediction was made, making it very attractive for operational use.

Decision trees also provide the foundation for more advanced ensemble methods such as bagging, random forests and gradient boosting.

In this tutorial, you will discover how to implement the Classification And Regression Tree algorithm from scratch with Python.

After completing this tutorial, you will know:

- How to calculate and evaluate candidate split points in a data.
- How to arrange splits into a decision tree structure.
- How to apply the classification and regression tree algorithm to a real problem.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Feb/2017**: Fixed a bug in build_tree.**Update Aug/2017**: Fixed a bug in Gini calculation, added the missing weighting of group Gini scores by group size (thanks Michael!).**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief introduction to the Classification and Regression Tree algorithm and the Banknote dataset used in this tutorial.

Classification and Regression Trees or CART for short is an acronym introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems.

We will focus on using CART for classification in this tutorial.

The representation of the CART model is a binary tree. This is the same binary tree from algorithms and data structures, nothing too fancy (each node can have zero, one or two child nodes).

A node represents a single input variable (X) and a split point on that variable, assuming the variable is numeric. The leaf nodes (also called terminal nodes) of the tree contain an output variable (y) which is used to make a prediction.

Once created, a tree can be navigated with a new row of data following each branch with the splits until a final prediction is made.

Creating a binary decision tree is actually a process of dividing up the input space. A greedy approach is used to divide the space called recursive binary splitting. This is a numerical procedure where all the values are lined up and different split points are tried and tested using a cost function.

The split with the best cost (lowest cost because we minimize cost) is selected. All input variables and all possible split points are evaluated and chosen in a greedy manner based on the cost function.

**Regression**: The cost function that is minimized to choose split points is the sum squared error across all training samples that fall within the rectangle.**Classification**: The Gini cost function is used which provides an indication of how pure the nodes are, where node purity refers to how mixed the training data assigned to each node is.

Splitting continues until nodes contain a minimum number of training examples or a maximum tree depth is reached.

The banknote dataset involves predicting whether a given banknote is authentic given a number of measures taken from a photograph.

The dataset contains 1,372 rows with 5 numeric variables. It is a classification problem with two classes (binary classification).

Below provides a list of the five variables in the dataset.

- variance of Wavelet Transformed image (continuous).
- skewness of Wavelet Transformed image (continuous).
- kurtosis of Wavelet Transformed image (continuous).
- entropy of image (continuous).
- class (integer).

Below is a sample of the first 5 rows of the dataset

3.6216,8.6661,-2.8073,-0.44699,0 4.5459,8.1674,-2.4586,-1.4621,0 3.866,-2.6383,1.9242,0.10645,0 3.4566,9.5228,-4.0112,-3.5944,0 0.32924,-4.4552,4.5718,-0.9888,0 4.3684,9.6718,-3.9606,-3.1625,0

Using the Zero Rule Algorithm to predict the most common class value, the baseline accuracy on the problem is about 50%.

You can learn more and download the dataset from the UCI Machine Learning Repository.

Download the dataset and place it in your current working directory with the filename **data_banknote_authentication.csv**.

This tutorial is broken down into 5 parts:

- Gini Index.
- Create Split.
- Build a Tree.
- Make a Prediction.
- Banknote Case Study.

These steps will give you the foundation that you need to implement the CART algorithm from scratch and apply it to your own predictive modeling problems.

The Gini index is the name of the cost function used to evaluate splits in the dataset.

A split in the dataset involves one input attribute and one value for that attribute. It can be used to divide training patterns into two groups of rows.

A Gini score gives an idea of how good a split is by how mixed the classes are in the two groups created by the split. A perfect separation results in a Gini score of 0, whereas the worst case split that results in 50/50 classes in each group result in a Gini score of 0.5 (for a 2 class problem).

Calculating Gini is best demonstrated with an example.

We have two groups of data with 2 rows in each group. The rows in the first group all belong to class 0 and the rows in the second group belong to class 1, so it’s a perfect split.

We first need to calculate the proportion of classes in each group.

proportion = count(class_value) / count(rows)

The proportions for this example would be:

group_1_class_0 = 2 / 2 = 1 group_1_class_1 = 0 / 2 = 0 group_2_class_0 = 0 / 2 = 0 group_2_class_1 = 2 / 2 = 1

Gini is then calculated for each child node as follows:

gini_index = sum(proportion * (1.0 - proportion)) gini_index = 1.0 - sum(proportion * proportion)

The Gini index for each group must then be weighted by the size of the group, relative to all of the samples in the parent, e.g. all samples that are currently being grouped. We can add this weighting to the Gini calculation for a group as follows:

gini_index = (1.0 - sum(proportion * proportion)) * (group_size/total_samples)

In this example the Gini scores for each group are calculated as follows:

Gini(group_1) = (1 - (1*1 + 0*0)) * 2/4 Gini(group_1) = 0.0 * 0.5 Gini(group_1) = 0.0 Gini(group_2) = (1 - (0*0 + 1*1)) * 2/4 Gini(group_2) = 0.0 * 0.5 Gini(group_2) = 0.0

The scores are then added across each child node at the split point to give a final Gini score for the split point that can be compared to other candidate split points.

The Gini for this split point would then be calculated as 0.0 + 0.0 or a perfect Gini score of 0.0.

Below is a function named **gini_index()** the calculates the Gini index for a list of groups and a list of known class values.

You can see that there are some safety checks in there to avoid a divide by zero for an empty group.

# Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini

We can test this function with our worked example above. We can also test it for the worst case of a 50/50 split in each group. The complete example is listed below.

# Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # test Gini values print(gini_index([[[1, 1], [1, 0]], [[1, 1], [1, 0]]], [0, 1])) print(gini_index([[[1, 0], [1, 0]], [[1, 1], [1, 1]]], [0, 1]))

Running the example prints the two Gini scores, first the score for the worst case at 0.5 followed by the score for the best case at 0.0.

0.5 0.0

Now that we know how to evaluate the results of a split, let’s look at creating splits.

A split is comprised of an attribute in the dataset and a value.

We can summarize this as the index of an attribute to split and the value by which to split rows on that attribute. This is just a useful shorthand for indexing into rows of data.

Creating a split involves three parts, the first we have already looked at which is calculating the Gini score. The remaining two parts are:

- Splitting a Dataset.
- Evaluating All Splits.

Let’s take a look at each.

Splitting a dataset means separating a dataset into two lists of rows given the index of an attribute and a split value for that attribute.

Once we have the two groups, we can then use our Gini score above to evaluate the cost of the split.

Splitting a dataset involves iterating over each row, checking if the attribute value is below or above the split value and assigning it to the left or right group respectively.

Below is a function named **test_split()** that implements this procedure.

# Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right

Not much to it.

Note that the right group contains all rows with a value at the index above or equal to the split value.

With the Gini function above and the test split function we now have everything we need to evaluate splits.

Given a dataset, we must check every value on each attribute as a candidate split, evaluate the cost of the split and find the best possible split we could make.

Once the best split is found, we can use it as a node in our decision tree.

This is an exhaustive and greedy algorithm.

We will use a dictionary to represent a node in the decision tree as we can store data by name. When selecting the best split and using it as a new node for the tree we will store the index of the chosen attribute, the value of that attribute by which to split and the two groups of data split by the chosen split point.

Each group of data is its own small dataset of just those rows assigned to the left or right group by the splitting process. You can imagine how we might split each group again, recursively as we build out our decision tree.

Below is a function named **get_split()** that implements this procedure. You can see that it iterates over each attribute (except the class value) and then each value for that attribute, splitting and evaluating splits as it goes.

The best split is recorded and then returned after all checks are complete.

# Select the best split point for a dataset def get_split(dataset): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None for index in range(len(dataset[0])-1): for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups}

We can contrive a small dataset to test out this function and our whole dataset splitting process.

X1 X2 Y 2.771244718 1.784783929 0 1.728571309 1.169761413 0 3.678319846 2.81281357 0 3.961043357 2.61995032 0 2.999208922 2.209014212 0 7.497545867 3.162953546 1 9.00220326 3.339047188 1 7.444542326 0.476683375 1 10.12493903 3.234550982 1 6.642287351 3.319983761 1

We can plot this dataset using separate colors for each class. You can see that it would not be difficult to manually pick a value of X1 (x-axis on the plot) to split this dataset.

The example below puts all of this together.

# Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right # Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # Select the best split point for a dataset def get_split(dataset): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None for index in range(len(dataset[0])-1): for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) print('X%d < %.3f Gini=%.3f' % ((index+1), row[index], gini)) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups} dataset = [[2.771244718,1.784783929,0], [1.728571309,1.169761413,0], [3.678319846,2.81281357,0], [3.961043357,2.61995032,0], [2.999208922,2.209014212,0], [7.497545867,3.162953546,1], [9.00220326,3.339047188,1], [7.444542326,0.476683375,1], [10.12493903,3.234550982,1], [6.642287351,3.319983761,1]] split = get_split(dataset) print('Split: [X%d < %.3f]' % ((split['index']+1), split['value']))

The **get_split()** function was modified to print out each split point and it’s Gini index as it was evaluated.

Running the example prints all of the Gini scores and then prints the score of best split in the dataset of X1 < 6.642 with a Gini Index of 0.0 or a perfect split.

X1 < 2.771 Gini=0.444 X1 < 1.729 Gini=0.500 X1 < 3.678 Gini=0.286 X1 < 3.961 Gini=0.167 X1 < 2.999 Gini=0.375 X1 < 7.498 Gini=0.286 X1 < 9.002 Gini=0.375 X1 < 7.445 Gini=0.167 X1 < 10.125 Gini=0.444 X1 < 6.642 Gini=0.000 X2 < 1.785 Gini=0.500 X2 < 1.170 Gini=0.444 X2 < 2.813 Gini=0.320 X2 < 2.620 Gini=0.417 X2 < 2.209 Gini=0.476 X2 < 3.163 Gini=0.167 X2 < 3.339 Gini=0.444 X2 < 0.477 Gini=0.500 X2 < 3.235 Gini=0.286 X2 < 3.320 Gini=0.375 Split: [X1 < 6.642]

Now that we know how to find the best split points in a dataset or list of rows, let’s see how we can use it to build out a decision tree.

Creating the root node of the tree is easy.

We call the above **get_split()** function using the entire dataset.

Adding more nodes to our tree is more interesting.

Building a tree may be divided into 3 main parts:

- Terminal Nodes.
- Recursive Splitting.
- Building a Tree.

We need to decide when to stop growing a tree.

We can do that using the depth and the number of rows that the node is responsible for in the training dataset.

**Maximum Tree Depth**. This is the maximum number of nodes from the root node of the tree. Once a maximum depth of the tree is met, we must stop splitting adding new nodes. Deeper trees are more complex and are more likely to overfit the training data.**Minimum Node Records**. This is the minimum number of training patterns that a given node is responsible for. Once at or below this minimum, we must stop splitting and adding new nodes. Nodes that account for too few training patterns are expected to be too specific and are likely to overfit the training data.

These two approaches will be user-specified arguments to our tree building procedure.

There is one more condition. It is possible to choose a split in which all rows belong to one group. In this case, we will be unable to continue splitting and adding child nodes as we will have no records to split on one side or another.

Now we have some ideas of when to stop growing the tree. When we do stop growing at a given point, that node is called a terminal node and is used to make a final prediction.

This is done by taking the group of rows assigned to that node and selecting the most common class value in the group. This will be used to make predictions.

Below is a function named **to_terminal()** that will select a class value for a group of rows. It returns the most common output value in a list of rows.

# Create a terminal node value def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count)

We know how and when to create terminal nodes, now we can build our tree.

Building a decision tree involves calling the above developed **get_split()** function over and over again on the groups created for each node.

New nodes added to an existing node are called child nodes. A node may have zero children (a terminal node), one child (one side makes a prediction directly) or two child nodes. We will refer to the child nodes as left and right in the dictionary representation of a given node.

Once a node is created, we can create child nodes recursively on each group of data from the split by calling the same function again.

Below is a function that implements this recursive procedure. It takes a node as an argument as well as the maximum depth, minimum number of patterns in a node and the current depth of a node.

You can imagine how this might be first called passing in the root node and the depth of 1. This function is best explained in steps:

- Firstly, the two groups of data split by the node are extracted for use and deleted from the node. As we work on these groups the node no longer requires access to these data.
- Next, we check if either left or right group of rows is empty and if so we create a terminal node using what records we do have.
- We then check if we have reached our maximum depth and if so we create a terminal node.
- We then process the left child, creating a terminal node if the group of rows is too small, otherwise creating and adding the left node in a depth first fashion until the bottom of the tree is reached on this branch.
- The right side is then processed in the same manner, as we rise back up the constructed tree to the root.

# Create child splits for a node or make terminal def split(node, max_depth, min_size, depth): left, right = node['groups'] del(node['groups']) # check for a no split if not left or not right: node['left'] = node['right'] = to_terminal(left + right) return # check for max depth if depth >= max_depth: node['left'], node['right'] = to_terminal(left), to_terminal(right) return # process left child if len(left) <= min_size: node['left'] = to_terminal(left) else: node['left'] = get_split(left) split(node['left'], max_depth, min_size, depth+1) # process right child if len(right) <= min_size: node['right'] = to_terminal(right) else: node['right'] = get_split(right) split(node['right'], max_depth, min_size, depth+1)

We can now put all of the pieces together.

Building the tree involves creating the root node and calling the **split()** function that then calls itself recursively to build out the whole tree.

Below is the small **build_tree()** function that implements this procedure.

# Build a decision tree def build_tree(train, max_depth, min_size): root = get_split(train) split(root, max_depth, min_size, 1) return root

We can test out this whole procedure using the small dataset we contrived above.

Below is the complete example.

Also included is a small **print_tree()** function that recursively prints out nodes of the decision tree with one line per node. Although not as striking as a real decision tree diagram, it gives an idea of the tree structure and decisions made throughout.

# Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right # Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # Select the best split point for a dataset def get_split(dataset): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None for index in range(len(dataset[0])-1): for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups} # Create a terminal node value def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count) # Create child splits for a node or make terminal def split(node, max_depth, min_size, depth): left, right = node['groups'] del(node['groups']) # check for a no split if not left or not right: node['left'] = node['right'] = to_terminal(left + right) return # check for max depth if depth >= max_depth: node['left'], node['right'] = to_terminal(left), to_terminal(right) return # process left child if len(left) <= min_size: node['left'] = to_terminal(left) else: node['left'] = get_split(left) split(node['left'], max_depth, min_size, depth+1) # process right child if len(right) <= min_size: node['right'] = to_terminal(right) else: node['right'] = get_split(right) split(node['right'], max_depth, min_size, depth+1) # Build a decision tree def build_tree(train, max_depth, min_size): root = get_split(train) split(root, max_depth, min_size, 1) return root # Print a decision tree def print_tree(node, depth=0): if isinstance(node, dict): print('%s[X%d < %.3f]' % ((depth*' ', (node['index']+1), node['value']))) print_tree(node['left'], depth+1) print_tree(node['right'], depth+1) else: print('%s[%s]' % ((depth*' ', node))) dataset = [[2.771244718,1.784783929,0], [1.728571309,1.169761413,0], [3.678319846,2.81281357,0], [3.961043357,2.61995032,0], [2.999208922,2.209014212,0], [7.497545867,3.162953546,1], [9.00220326,3.339047188,1], [7.444542326,0.476683375,1], [10.12493903,3.234550982,1], [6.642287351,3.319983761,1]] tree = build_tree(dataset, 1, 1) print_tree(tree)

We can vary the maximum depth argument as we run this example and see the effect on the printed tree.

With a maximum depth of 1 (the second parameter in the call to the **build_tree()** function), we can see that the tree uses the perfect split we discovered in the previous section. This is a tree with one node, also called a decision stump.

[X1 < 6.642] [0] [1]

Increasing the maximum depth to 2, we are forcing the tree to make splits even when none are required. The **X1** attribute is then used again by both the left and right children of the root node to split up the already perfect mix of classes.

[X1 < 6.642] [X1 < 2.771] [0] [0] [X1 < 7.498] [1] [1]

Finally, and perversely, we can force one more level of splits with a maximum depth of 3.

[X1 < 6.642] [X1 < 2.771] [0] [X1 < 2.771] [0] [0] [X1 < 7.498] [X1 < 7.445] [1] [1] [X1 < 7.498] [1] [1]

These tests show that there is great opportunity to refine the implementation to avoid unnecessary splits. This is left as an extension.

Now that we can create a decision tree, let’s see how we can use it to make predictions on new data.

Making predictions with a decision tree involves navigating the tree with the specifically provided row of data.

Again, we can implement this using a recursive function, where the same prediction routine is called again with the left or the right child nodes, depending on how the split affects the provided data.

We must check if a child node is either a terminal value to be returned as the prediction, or if it is a dictionary node containing another level of the tree to be considered.

Below is the **predict()** function that implements this procedure. You can see how the index and value in a given node

You can see how the index and value in a given node is used to evaluate whether the row of provided data falls on the left or the right of the split.

# Make a prediction with a decision tree def predict(node, row): if row[node['index']] < node['value']: if isinstance(node['left'], dict): return predict(node['left'], row) else: return node['left'] else: if isinstance(node['right'], dict): return predict(node['right'], row) else: return node['right']

We can use our contrived dataset to test this function. Below is an example that uses a hard-coded decision tree with a single node that best splits the data (a decision stump).

The example makes a prediction for each row in the dataset.

# Make a prediction with a decision tree def predict(node, row): if row[node['index']] < node['value']: if isinstance(node['left'], dict): return predict(node['left'], row) else: return node['left'] else: if isinstance(node['right'], dict): return predict(node['right'], row) else: return node['right'] dataset = [[2.771244718,1.784783929,0], [1.728571309,1.169761413,0], [3.678319846,2.81281357,0], [3.961043357,2.61995032,0], [2.999208922,2.209014212,0], [7.497545867,3.162953546,1], [9.00220326,3.339047188,1], [7.444542326,0.476683375,1], [10.12493903,3.234550982,1], [6.642287351,3.319983761,1]] # predict with a stump stump = {'index': 0, 'right': 1, 'value': 6.642287351, 'left': 0} for row in dataset: prediction = predict(stump, row) print('Expected=%d, Got=%d' % (row[-1], prediction))

Running the example prints the correct prediction for each row, as expected.

Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1

We now know how to create a decision tree and use it to make predictions. Now, let’s apply it to a real dataset.

This section applies the CART algorithm to the Bank Note dataset.

The first step is to load the dataset and convert the loaded data to numbers that we can use to calculate split points. For this we will use the helper function **load_csv()** to load the file and **str_column_to_float()** to convert string numbers to floats.

We will evaluate the algorithm using k-fold cross-validation with 5 folds. This means that 1372/5=274.4 or just over 270 records will be used in each fold. We will use the helper functions **evaluate_algorithm()** to evaluate the algorithm with cross-validation and **accuracy_metric()** to calculate the accuracy of predictions.

A new function named **decision_tree()** was developed to manage the application of the CART algorithm, first creating the tree from the training dataset, then using the tree to make predictions on a test dataset.

The complete example is listed below.

# CART on the Bank Note dataset from random import seed from random import randrange from csv import reader # Load a CSV file def load_csv(filename): file = open(filename, "rt") lines = reader(file) dataset = list(lines) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Split a dataset based on an attribute and an attribute value def test_split(index, value, dataset): left, right = list(), list() for row in dataset: if row[index] < value: left.append(row) else: right.append(row) return left, right # Calculate the Gini index for a split dataset def gini_index(groups, classes): # count all samples at split point n_instances = float(sum([len(group) for group in groups])) # sum weighted Gini index for each group gini = 0.0 for group in groups: size = float(len(group)) # avoid divide by zero if size == 0: continue score = 0.0 # score the group based on the score for each class for class_val in classes: p = [row[-1] for row in group].count(class_val) / size score += p * p # weight the group score by its relative size gini += (1.0 - score) * (size / n_instances) return gini # Select the best split point for a dataset def get_split(dataset): class_values = list(set(row[-1] for row in dataset)) b_index, b_value, b_score, b_groups = 999, 999, 999, None for index in range(len(dataset[0])-1): for row in dataset: groups = test_split(index, row[index], dataset) gini = gini_index(groups, class_values) if gini < b_score: b_index, b_value, b_score, b_groups = index, row[index], gini, groups return {'index':b_index, 'value':b_value, 'groups':b_groups} # Create a terminal node value def to_terminal(group): outcomes = [row[-1] for row in group] return max(set(outcomes), key=outcomes.count) # Create child splits for a node or make terminal def split(node, max_depth, min_size, depth): left, right = node['groups'] del(node['groups']) # check for a no split if not left or not right: node['left'] = node['right'] = to_terminal(left + right) return # check for max depth if depth >= max_depth: node['left'], node['right'] = to_terminal(left), to_terminal(right) return # process left child if len(left) <= min_size: node['left'] = to_terminal(left) else: node['left'] = get_split(left) split(node['left'], max_depth, min_size, depth+1) # process right child if len(right) <= min_size: node['right'] = to_terminal(right) else: node['right'] = get_split(right) split(node['right'], max_depth, min_size, depth+1) # Build a decision tree def build_tree(train, max_depth, min_size): root = get_split(train) split(root, max_depth, min_size, 1) return root # Make a prediction with a decision tree def predict(node, row): if row[node['index']] < node['value']: if isinstance(node['left'], dict): return predict(node['left'], row) else: return node['left'] else: if isinstance(node['right'], dict): return predict(node['right'], row) else: return node['right'] # Classification and Regression Tree Algorithm def decision_tree(train, test, max_depth, min_size): tree = build_tree(train, max_depth, min_size) predictions = list() for row in test: prediction = predict(tree, row) predictions.append(prediction) return(predictions) # Test CART on Bank Note dataset seed(1) # load and prepare data filename = 'data_banknote_authentication.csv' dataset = load_csv(filename) # convert string attributes to integers for i in range(len(dataset[0])): str_column_to_float(dataset, i) # evaluate algorithm n_folds = 5 max_depth = 5 min_size = 10 scores = evaluate_algorithm(dataset, decision_tree, n_folds, max_depth, min_size) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

The example uses the max tree depth of 5 layers and the minimum number of rows per node to 10. These parameters to CART were chosen with a little experimentation, but are by no means are they optimal.

Running the example prints the average classification accuracy on each fold as well as the average performance across all folds.

You can see that CART and the chosen configuration achieved a mean classification accuracy of about 97% which is dramatically better than the Zero Rule algorithm that achieved 50% accuracy.

Scores: [96.35036496350365, 97.08029197080292, 97.44525547445255, 98.17518248175182, 97.44525547445255] Mean Accuracy: 97.299%

This section lists extensions to this tutorial that you may wish to explore.

**Algorithm Tuning**. The application of CART to the Bank Note dataset was not tuned. Experiment with different parameter values and see if you can achieve better performance.**Cross Entropy**. Another cost function for evaluating splits is cross entropy (logloss). You could implement and experiment with this alternative cost function.**Tree Pruning**. An important technique for reducing overfitting of the training dataset is to prune the trees. Investigate and implement tree pruning methods.**Categorical Dataset**. The example was designed for input data with numerical or ordinal input attributes, experiment with categorical input data and splits that may use equality instead of ranking.**Regression**. Adapt the tree for regression using a different cost function and method for creating terminal nodes.**More Datasets**. Apply the algorithm to more datasets on the UCI Machine Learning Repository.

**Did you explore any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement the decision tree algorithm from scratch with Python.

Specifically, you learned:

- How to select and evaluate split points in a training dataset.
- How to recursively build a decision tree from multiple splits.
- How to apply the CART algorithm to a real world classification predictive modeling problem.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer them.

The post How To Implement The Decision Tree Algorithm From Scratch In Python appeared first on Machine Learning Mastery.

]]>The post How to Code a Neural Network with Backpropagation In Python (from scratch) appeared first on Machine Learning Mastery.

]]>It is the technique still used to train large deep learning networks.

In this tutorial, you will discover how to implement the backpropagation algorithm for a neural network from scratch with Python.

After completing this tutorial, you will know:

- How to forward-propagate an input to calculate an output.
- How to back-propagate error and train a network.
- How to apply the backpropagation algorithm to a real-world predictive modeling problem.

Let’s get started.

**Update Nov/2016**: Fixed a bug in the activate() function. Thanks Alex!**Update Jan/2017**: Fixes issues with Python 3.**Update Jan/2017**: Updated small bug in update_weights(). Thanks Tomasz!**Update Apr/2018**: Added direct link to CSV dataset.**Update Aug/2018**: Tested and updated to work with Python 3.6.**Update Sep/2019**: Updated wheat-seeds.csv to fix formatting issues.

This section provides a brief introduction to the Backpropagation Algorithm and the Wheat Seeds dataset that we will be using in this tutorial.

The Backpropagation algorithm is a supervised learning method for multilayer feed-forward networks from the field of Artificial Neural Networks.

Feed-forward neural networks are inspired by the information processing of one or more neural cells, called a neuron. A neuron accepts input signals via its dendrites, which pass the electrical signal down to the cell body. The axon carries the signal out to synapses, which are the connections of a cell’s axon to other cell’s dendrites.

The principle of the backpropagation approach is to model a given function by modifying internal weightings of input signals to produce an expected output signal. The system is trained using a supervised learning method, where the error between the system’s output and a known expected output is presented to the system and used to modify its internal state.

Technically, the backpropagation algorithm is a method for training the weights in a multilayer feed-forward neural network. As such, it requires a network structure to be defined of one or more layers where one layer is fully connected to the next layer. A standard network structure is one input layer, one hidden layer, and one output layer.

Backpropagation can be used for both classification and regression problems, but we will focus on classification in this tutorial.

In classification problems, best results are achieved when the network has one neuron in the output layer for each class value. For example, a 2-class or binary classification problem with the class values of A and B. These expected outputs would have to be transformed into binary vectors with one column for each class value. Such as [1, 0] and [0, 1] for A and B respectively. This is called a one hot encoding.

The seeds dataset involves the prediction of species given measurements seeds from different varieties of wheat.

There are 201 records and 7 numerical input variables. It is a classification problem with 3 output classes. The scale for each numeric input value vary, so some data normalization may be required for use with algorithms that weight inputs like the backpropagation algorithm.

Below is a sample of the first 5 rows of the dataset.

15.26,14.84,0.871,5.763,3.312,2.221,5.22,1 14.88,14.57,0.8811,5.554,3.333,1.018,4.956,1 14.29,14.09,0.905,5.291,3.337,2.699,4.825,1 13.84,13.94,0.8955,5.324,3.379,2.259,4.805,1 16.14,14.99,0.9034,5.658,3.562,1.355,5.175,1

Using the Zero Rule algorithm that predicts the most common class value, the baseline accuracy for the problem is 28.095%.

You can learn more and download the seeds dataset from the UCI Machine Learning Repository.

Download the seeds dataset and place it into your current working directory with the filename **seeds_dataset.csv**.

The dataset is in tab-separated format, so you must convert it to CSV using a text editor or a spreadsheet program.

Update, download the dataset in CSV format directly:

This tutorial is broken down into 6 parts:

- Initialize Network.
- Forward Propagate.
- Back Propagate Error.
- Train Network.
- Predict.
- Seeds Dataset Case Study.

These steps will provide the foundation that you need to implement the backpropagation algorithm from scratch and apply it to your own predictive modeling problems.

Let’s start with something easy, the creation of a new network ready for training.

Each neuron has a set of weights that need to be maintained. One weight for each input connection and an additional weight for the bias. We will need to store additional properties for a neuron during training, therefore we will use a dictionary to represent each neuron and store properties by names such as ‘**weights**‘ for the weights.

A network is organized into layers. The input layer is really just a row from our training dataset. The first real layer is the hidden layer. This is followed by the output layer that has one neuron for each class value.

We will organize layers as arrays of dictionaries and treat the whole network as an array of layers.

It is good practice to initialize the network weights to small random numbers. In this case, will we use random numbers in the range of 0 to 1.

Below is a function named **initialize_network()** that creates a new neural network ready for training. It accepts three parameters, the number of inputs, the number of neurons to have in the hidden layer and the number of outputs.

You can see that for the hidden layer we create **n_hidden** neurons and each neuron in the hidden layer has **n_inputs + 1** weights, one for each input column in a dataset and an additional one for the bias.

You can also see that the output layer that connects to the hidden layer has **n_outputs** neurons, each with **n_hidden + 1** weights. This means that each neuron in the output layer connects to (has a weight for) each neuron in the hidden layer.

# Initialize a network def initialize_network(n_inputs, n_hidden, n_outputs): network = list() hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)] network.append(hidden_layer) output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)] network.append(output_layer) return network

Let’s test out this function. Below is a complete example that creates a small network.

from random import seed from random import random # Initialize a network def initialize_network(n_inputs, n_hidden, n_outputs): network = list() hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)] network.append(hidden_layer) output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)] network.append(output_layer) return network seed(1) network = initialize_network(2, 1, 2) for layer in network: print(layer)

Running the example, you can see that the code prints out each layer one by one. You can see the hidden layer has one neuron with 2 input weights plus the bias. The output layer has 2 neurons, each with 1 weight plus the bias.

[{'weights': [0.13436424411240122, 0.8474337369372327, 0.763774618976614]}] [{'weights': [0.2550690257394217, 0.49543508709194095]}, {'weights': [0.4494910647887381, 0.651592972722763]}]

Now that we know how to create and initialize a network, let’s see how we can use it to calculate an output.

We can calculate an output from a neural network by propagating an input signal through each layer until the output layer outputs its values.

We call this forward-propagation.

It is the technique we will need to generate predictions during training that will need to be corrected, and it is the method we will need after the network is trained to make predictions on new data.

We can break forward propagation down into three parts:

- Neuron Activation.
- Neuron Transfer.
- Forward Propagation.

The first step is to calculate the activation of one neuron given an input.

The input could be a row from our training dataset, as in the case of the hidden layer. It may also be the outputs from each neuron in the hidden layer, in the case of the output layer.

Neuron activation is calculated as the weighted sum of the inputs. Much like linear regression.

activation = sum(weight_i * input_i) + bias

Where **weight** is a network weight, **input** is an input, **i** is the index of a weight or an input and **bias** is a special weight that has no input to multiply with (or you can think of the input as always being 1.0).

Below is an implementation of this in a function named **activate()**. You can see that the function assumes that the bias is the last weight in the list of weights. This helps here and later to make the code easier to read.

# Calculate neuron activation for an input def activate(weights, inputs): activation = weights[-1] for i in range(len(weights)-1): activation += weights[i] * inputs[i] return activation

Now, let’s see how to use the neuron activation.

Once a neuron is activated, we need to transfer the activation to see what the neuron output actually is.

Different transfer functions can be used. It is traditional to use the sigmoid activation function, but you can also use the tanh (hyperbolic tangent) function to transfer outputs. More recently, the rectifier transfer function has been popular with large deep learning networks.

The sigmoid activation function looks like an S shape, it’s also called the logistic function. It can take any input value and produce a number between 0 and 1 on an S-curve. It is also a function of which we can easily calculate the derivative (slope) that we will need later when backpropagating error.

We can transfer an activation function using the sigmoid function as follows:

output = 1 / (1 + e^(-activation))

Where **e** is the base of the natural logarithms (Euler’s number).

Below is a function named **transfer()** that implements the sigmoid equation.

# Transfer neuron activation def transfer(activation): return 1.0 / (1.0 + exp(-activation))

Now that we have the pieces, let’s see how they are used.

Forward propagating an input is straightforward.

We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer.

Below is a function named **forward_propagate()** that implements the forward propagation for a row of data from our dataset with our neural network.

You can see that a neuron’s output value is stored in the neuron with the name ‘**output**‘. You can also see that we collect the outputs for a layer in an array named **new_inputs** that becomes the array **inputs** and is used as inputs for the following layer.

The function returns the outputs from the last layer also called the output layer.

# Forward propagate input to a network output def forward_propagate(network, row): inputs = row for layer in network: new_inputs = [] for neuron in layer: activation = activate(neuron['weights'], inputs) neuron['output'] = transfer(activation) new_inputs.append(neuron['output']) inputs = new_inputs return inputs

Let’s put all of these pieces together and test out the forward propagation of our network.

We define our network inline with one hidden neuron that expects 2 input values and an output layer with two neurons.

from math import exp # Calculate neuron activation for an input def activate(weights, inputs): activation = weights[-1] for i in range(len(weights)-1): activation += weights[i] * inputs[i] return activation # Transfer neuron activation def transfer(activation): return 1.0 / (1.0 + exp(-activation)) # Forward propagate input to a network output def forward_propagate(network, row): inputs = row for layer in network: new_inputs = [] for neuron in layer: activation = activate(neuron['weights'], inputs) neuron['output'] = transfer(activation) new_inputs.append(neuron['output']) inputs = new_inputs return inputs # test forward propagation network = [[{'weights': [0.13436424411240122, 0.8474337369372327, 0.763774618976614]}], [{'weights': [0.2550690257394217, 0.49543508709194095]}, {'weights': [0.4494910647887381, 0.651592972722763]}]] row = [1, 0, None] output = forward_propagate(network, row) print(output)

Running the example propagates the input pattern [1, 0] and produces an output value that is printed. Because the output layer has two neurons, we get a list of two numbers as output.

The actual output values are just nonsense for now, but next, we will start to learn how to make the weights in the neurons more useful.

[0.6629970129852887, 0.7253160725279748]

The backpropagation algorithm is named for the way in which weights are trained.

Error is calculated between the expected outputs and the outputs forward propagated from the network. These errors are then propagated backward through the network from the output layer to the hidden layer, assigning blame for the error and updating weights as they go.

The math for backpropagating error is rooted in calculus, but we will remain high level in this section and focus on what is calculated and how rather than why the calculations take this particular form.

This part is broken down into two sections.

- Transfer Derivative.
- Error Backpropagation.

Given an output value from a neuron, we need to calculate it’s slope.

We are using the sigmoid transfer function, the derivative of which can be calculated as follows:

derivative = output * (1.0 - output)

Below is a function named **transfer_derivative()** that implements this equation.

# Calculate the derivative of an neuron output def transfer_derivative(output): return output * (1.0 - output)

Now, let’s see how this can be used.

The first step is to calculate the error for each output neuron, this will give us our error signal (input) to propagate backwards through the network.

The error for a given neuron can be calculated as follows:

error = (expected - output) * transfer_derivative(output)

Where **expected** is the expected output value for the neuron, **output** is the output value for the neuron and **transfer_derivative()** calculates the slope of the neuron’s output value, as shown above.

This error calculation is used for neurons in the output layer. The expected value is the class value itself. In the hidden layer, things are a little more complicated.

The error signal for a neuron in the hidden layer is calculated as the weighted error of each neuron in the output layer. Think of the error traveling back along the weights of the output layer to the neurons in the hidden layer.

The back-propagated error signal is accumulated and then used to determine the error for the neuron in the hidden layer, as follows:

error = (weight_k * error_j) * transfer_derivative(output)

Where **error_j** is the error signal from the **j**th neuron in the output layer, **weight_k** is the weight that connects the **k**th neuron to the current neuron and output is the output for the current neuron.

Below is a function named **backward_propagate_error()** that implements this procedure.

You can see that the error signal calculated for each neuron is stored with the name ‘delta’. You can see that the layers of the network are iterated in reverse order, starting at the output and working backwards. This ensures that the neurons in the output layer have ‘delta’ values calculated first that neurons in the hidden layer can use in the subsequent iteration. I chose the name ‘delta’ to reflect the change the error implies on the neuron (e.g. the weight delta).

You can see that the error signal for neurons in the hidden layer is accumulated from neurons in the output layer where the hidden neuron number **j** is also the index of the neuron’s weight in the output layer **neuron[‘weights’][j]**.

# Backpropagate error and store in neurons def backward_propagate_error(network, expected): for i in reversed(range(len(network))): layer = network[i] errors = list() if i != len(network)-1: for j in range(len(layer)): error = 0.0 for neuron in network[i + 1]: error += (neuron['weights'][j] * neuron['delta']) errors.append(error) else: for j in range(len(layer)): neuron = layer[j] errors.append(expected[j] - neuron['output']) for j in range(len(layer)): neuron = layer[j] neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])

Let’s put all of the pieces together and see how it works.

We define a fixed neural network with output values and backpropagate an expected output pattern. The complete example is listed below.

# Calculate the derivative of an neuron output def transfer_derivative(output): return output * (1.0 - output) # Backpropagate error and store in neurons def backward_propagate_error(network, expected): for i in reversed(range(len(network))): layer = network[i] errors = list() if i != len(network)-1: for j in range(len(layer)): error = 0.0 for neuron in network[i + 1]: error += (neuron['weights'][j] * neuron['delta']) errors.append(error) else: for j in range(len(layer)): neuron = layer[j] errors.append(expected[j] - neuron['output']) for j in range(len(layer)): neuron = layer[j] neuron['delta'] = errors[j] * transfer_derivative(neuron['output']) # test backpropagation of error network = [[{'output': 0.7105668883115941, 'weights': [0.13436424411240122, 0.8474337369372327, 0.763774618976614]}], [{'output': 0.6213859615555266, 'weights': [0.2550690257394217, 0.49543508709194095]}, {'output': 0.6573693455986976, 'weights': [0.4494910647887381, 0.651592972722763]}]] expected = [0, 1] backward_propagate_error(network, expected) for layer in network: print(layer)

Running the example prints the network after the backpropagation of error is complete. You can see that error values are calculated and stored in the neurons for the output layer and the hidden layer.

[{'output': 0.7105668883115941, 'weights': [0.13436424411240122, 0.8474337369372327, 0.763774618976614], 'delta': -0.0005348048046610517}] [{'output': 0.6213859615555266, 'weights': [0.2550690257394217, 0.49543508709194095], 'delta': -0.14619064683582808}, {'output': 0.6573693455986976, 'weights': [0.4494910647887381, 0.651592972722763], 'delta': 0.0771723774346327}]

Now let’s use the backpropagation of error to train the network.

The network is trained using stochastic gradient descent.

This involves multiple iterations of exposing a training dataset to the network and for each row of data forward propagating the inputs, backpropagating the error and updating the network weights.

This part is broken down into two sections:

- Update Weights.
- Train Network.

Once errors are calculated for each neuron in the network via the back propagation method above, they can be used to update weights.

Network weights are updated as follows:

weight = weight + learning_rate * error * input

Where **weight** is a given weight, **learning_rate** is a parameter that you must specify, **error** is the error calculated by the backpropagation procedure for the neuron and **input** is the input value that caused the error.

The same procedure can be used for updating the bias weight, except there is no input term, or input is the fixed value of 1.0.

Learning rate controls how much to change the weight to correct for the error. For example, a value of 0.1 will update the weight 10% of the amount that it possibly could be updated. Small learning rates are preferred that cause slower learning over a large number of training iterations. This increases the likelihood of the network finding a good set of weights across all layers rather than the fastest set of weights that minimize error (called premature convergence).

Below is a function named **update_weights()** that updates the weights for a network given an input row of data, a learning rate and assume that a forward and backward propagation have already been performed.

Remember that the input for the output layer is a collection of outputs from the hidden layer.

# Update network weights with error def update_weights(network, row, l_rate): for i in range(len(network)): inputs = row[:-1] if i != 0: inputs = [neuron['output'] for neuron in network[i - 1]] for neuron in network[i]: for j in range(len(inputs)): neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j] neuron['weights'][-1] += l_rate * neuron['delta']

Now we know how to update network weights, let’s see how we can do it repeatedly.

As mentioned, the network is updated using stochastic gradient descent.

This involves first looping for a fixed number of epochs and within each epoch updating the network for each row in the training dataset.

Because updates are made for each training pattern, this type of learning is called online learning. If errors were accumulated across an epoch before updating the weights, this is called batch learning or batch gradient descent.

Below is a function that implements the training of an already initialized neural network with a given training dataset, learning rate, fixed number of epochs and an expected number of output values.

The expected number of output values is used to transform class values in the training data into a one hot encoding. That is a binary vector with one column for each class value to match the output of the network. This is required to calculate the error for the output layer.

You can also see that the sum squared error between the expected output and the network output is accumulated each epoch and printed. This is helpful to create a trace of how much the network is learning and improving each epoch.

# Train a network for a fixed number of epochs def train_network(network, train, l_rate, n_epoch, n_outputs): for epoch in range(n_epoch): sum_error = 0 for row in train: outputs = forward_propagate(network, row) expected = [0 for i in range(n_outputs)] expected[row[-1]] = 1 sum_error += sum([(expected[i]-outputs[i])**2 for i in range(len(expected))]) backward_propagate_error(network, expected) update_weights(network, row, l_rate) print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error))

We now have all of the pieces to train the network. We can put together an example that includes everything we’ve seen so far including network initialization and train a network on a small dataset.

Below is a small contrived dataset that we can use to test out training our neural network.

X1 X2 Y 2.7810836 2.550537003 0 1.465489372 2.362125076 0 3.396561688 4.400293529 0 1.38807019 1.850220317 0 3.06407232 3.005305973 0 7.627531214 2.759262235 1 5.332441248 2.088626775 1 6.922596716 1.77106367 1 8.675418651 -0.242068655 1 7.673756466 3.508563011 1

Below is the complete example. We will use 2 neurons in the hidden layer. It is a binary classification problem (2 classes) so there will be two neurons in the output layer. The network will be trained for 20 epochs with a learning rate of 0.5, which is high because we are training for so few iterations.

from math import exp from random import seed from random import random # Initialize a network def initialize_network(n_inputs, n_hidden, n_outputs): network = list() hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)] network.append(hidden_layer) output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)] network.append(output_layer) return network # Calculate neuron activation for an input def activate(weights, inputs): activation = weights[-1] for i in range(len(weights)-1): activation += weights[i] * inputs[i] return activation # Transfer neuron activation def transfer(activation): return 1.0 / (1.0 + exp(-activation)) # Forward propagate input to a network output def forward_propagate(network, row): inputs = row for layer in network: new_inputs = [] for neuron in layer: activation = activate(neuron['weights'], inputs) neuron['output'] = transfer(activation) new_inputs.append(neuron['output']) inputs = new_inputs return inputs # Calculate the derivative of an neuron output def transfer_derivative(output): return output * (1.0 - output) # Backpropagate error and store in neurons def backward_propagate_error(network, expected): for i in reversed(range(len(network))): layer = network[i] errors = list() if i != len(network)-1: for j in range(len(layer)): error = 0.0 for neuron in network[i + 1]: error += (neuron['weights'][j] * neuron['delta']) errors.append(error) else: for j in range(len(layer)): neuron = layer[j] errors.append(expected[j] - neuron['output']) for j in range(len(layer)): neuron = layer[j] neuron['delta'] = errors[j] * transfer_derivative(neuron['output']) # Update network weights with error def update_weights(network, row, l_rate): for i in range(len(network)): inputs = row[:-1] if i != 0: inputs = [neuron['output'] for neuron in network[i - 1]] for neuron in network[i]: for j in range(len(inputs)): neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j] neuron['weights'][-1] += l_rate * neuron['delta'] # Train a network for a fixed number of epochs def train_network(network, train, l_rate, n_epoch, n_outputs): for epoch in range(n_epoch): sum_error = 0 for row in train: outputs = forward_propagate(network, row) expected = [0 for i in range(n_outputs)] expected[row[-1]] = 1 sum_error += sum([(expected[i]-outputs[i])**2 for i in range(len(expected))]) backward_propagate_error(network, expected) update_weights(network, row, l_rate) print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)) # Test training backprop algorithm seed(1) dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] n_inputs = len(dataset[0]) - 1 n_outputs = len(set([row[-1] for row in dataset])) network = initialize_network(n_inputs, 2, n_outputs) train_network(network, dataset, 0.5, 20, n_outputs) for layer in network: print(layer)

Running the example first prints the sum squared error each training epoch. We can see a trend of this error decreasing with each epoch.

Once trained, the network is printed, showing the learned weights. Also still in the network are output and delta values that can be ignored. We could update our training function to delete these data if we wanted.

>epoch=0, lrate=0.500, error=6.350 >epoch=1, lrate=0.500, error=5.531 >epoch=2, lrate=0.500, error=5.221 >epoch=3, lrate=0.500, error=4.951 >epoch=4, lrate=0.500, error=4.519 >epoch=5, lrate=0.500, error=4.173 >epoch=6, lrate=0.500, error=3.835 >epoch=7, lrate=0.500, error=3.506 >epoch=8, lrate=0.500, error=3.192 >epoch=9, lrate=0.500, error=2.898 >epoch=10, lrate=0.500, error=2.626 >epoch=11, lrate=0.500, error=2.377 >epoch=12, lrate=0.500, error=2.153 >epoch=13, lrate=0.500, error=1.953 >epoch=14, lrate=0.500, error=1.774 >epoch=15, lrate=0.500, error=1.614 >epoch=16, lrate=0.500, error=1.472 >epoch=17, lrate=0.500, error=1.346 >epoch=18, lrate=0.500, error=1.233 >epoch=19, lrate=0.500, error=1.132 [{'weights': [-1.4688375095432327, 1.850887325439514, 1.0858178629550297], 'output': 0.029980305604426185, 'delta': -0.0059546604162323625}, {'weights': [0.37711098142462157, -0.0625909894552989, 0.2765123702642716], 'output': 0.9456229000211323, 'delta': 0.0026279652850863837}] [{'weights': [2.515394649397849, -0.3391927502445985, -0.9671565426390275], 'output': 0.23648794202357587, 'delta': -0.04270059278364587}, {'weights': [-2.5584149848484263, 1.0036422106209202, 0.42383086467582715], 'output': 0.7790535202438367, 'delta': 0.03803132596437354}]

Once a network is trained, we need to use it to make predictions.

Making predictions with a trained neural network is easy enough.

We have already seen how to forward-propagate an input pattern to get an output. This is all we need to do to make a prediction. We can use the output values themselves directly as the probability of a pattern belonging to each output class.

It may be more useful to turn this output back into a crisp class prediction. We can do this by selecting the class value with the larger probability. This is also called the arg max function.

Below is a function named **predict()** that implements this procedure. It returns the index in the network output that has the largest probability. It assumes that class values have been converted to integers starting at 0.

# Make a prediction with a network def predict(network, row): outputs = forward_propagate(network, row) return outputs.index(max(outputs))

We can put this together with our code above for forward propagating input and with our small contrived dataset to test making predictions with an already-trained network. The example hardcodes a network trained from the previous step.

The complete example is listed below.

from math import exp # Calculate neuron activation for an input def activate(weights, inputs): activation = weights[-1] for i in range(len(weights)-1): activation += weights[i] * inputs[i] return activation # Transfer neuron activation def transfer(activation): return 1.0 / (1.0 + exp(-activation)) # Forward propagate input to a network output def forward_propagate(network, row): inputs = row for layer in network: new_inputs = [] for neuron in layer: activation = activate(neuron['weights'], inputs) neuron['output'] = transfer(activation) new_inputs.append(neuron['output']) inputs = new_inputs return inputs # Make a prediction with a network def predict(network, row): outputs = forward_propagate(network, row) return outputs.index(max(outputs)) # Test making predictions with the network dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] network = [[{'weights': [-1.482313569067226, 1.8308790073202204, 1.078381922048799]}, {'weights': [0.23244990332399884, 0.3621998343835864, 0.40289821191094327]}], [{'weights': [2.5001872433501404, 0.7887233511355132, -1.1026649757805829]}, {'weights': [-2.429350576245497, 0.8357651039198697, 1.0699217181280656]}]] for row in dataset: prediction = predict(network, row) print('Expected=%d, Got=%d' % (row[-1], prediction))

Running the example prints the expected output for each record in the training dataset, followed by the crisp prediction made by the network.

It shows that the network achieves 100% accuracy on this small dataset.

Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=0, Got=0 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1 Expected=1, Got=1

Now we are ready to apply our backpropagation algorithm to a real world dataset.

This section applies the Backpropagation algorithm to the wheat seeds dataset.

The first step is to load the dataset and convert the loaded data to numbers that we can use in our neural network. For this we will use the helper function **load_csv()** to load the file, **str_column_to_float()** to convert string numbers to floats and **str_column_to_int()** to convert the class column to integer values.

Input values vary in scale and need to be normalized to the range of 0 and 1. It is generally good practice to normalize input values to the range of the chosen transfer function, in this case, the sigmoid function that outputs values between 0 and 1. The **dataset_minmax()** and **normalize_dataset()** helper functions were used to normalize the input values.

We will evaluate the algorithm using k-fold cross-validation with 5 folds. This means that 201/5=40.2 or 40 records will be in each fold. We will use the helper functions **evaluate_algorithm()** to evaluate the algorithm with cross-validation and **accuracy_metric()** to calculate the accuracy of predictions.

A new function named **back_propagation()** was developed to manage the application of the Backpropagation algorithm, first initializing a network, training it on the training dataset and then using the trained network to make predictions on a test dataset.

The complete example is listed below.

# Backprop on the Seeds Dataset from random import seed from random import randrange from random import random from csv import reader from math import exp # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Find the min and max values for each column def dataset_minmax(dataset): minmax = list() stats = [[min(column), max(column)] for column in zip(*dataset)] return stats # Rescale dataset columns to the range 0-1 def normalize_dataset(dataset, minmax): for row in dataset: for i in range(len(row)-1): row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0]) # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Calculate neuron activation for an input def activate(weights, inputs): activation = weights[-1] for i in range(len(weights)-1): activation += weights[i] * inputs[i] return activation # Transfer neuron activation def transfer(activation): return 1.0 / (1.0 + exp(-activation)) # Forward propagate input to a network output def forward_propagate(network, row): inputs = row for layer in network: new_inputs = [] for neuron in layer: activation = activate(neuron['weights'], inputs) neuron['output'] = transfer(activation) new_inputs.append(neuron['output']) inputs = new_inputs return inputs # Calculate the derivative of an neuron output def transfer_derivative(output): return output * (1.0 - output) # Backpropagate error and store in neurons def backward_propagate_error(network, expected): for i in reversed(range(len(network))): layer = network[i] errors = list() if i != len(network)-1: for j in range(len(layer)): error = 0.0 for neuron in network[i + 1]: error += (neuron['weights'][j] * neuron['delta']) errors.append(error) else: for j in range(len(layer)): neuron = layer[j] errors.append(expected[j] - neuron['output']) for j in range(len(layer)): neuron = layer[j] neuron['delta'] = errors[j] * transfer_derivative(neuron['output']) # Update network weights with error def update_weights(network, row, l_rate): for i in range(len(network)): inputs = row[:-1] if i != 0: inputs = [neuron['output'] for neuron in network[i - 1]] for neuron in network[i]: for j in range(len(inputs)): neuron['weights'][j] += l_rate * neuron['delta'] * inputs[j] neuron['weights'][-1] += l_rate * neuron['delta'] # Train a network for a fixed number of epochs def train_network(network, train, l_rate, n_epoch, n_outputs): for epoch in range(n_epoch): for row in train: outputs = forward_propagate(network, row) expected = [0 for i in range(n_outputs)] expected[row[-1]] = 1 backward_propagate_error(network, expected) update_weights(network, row, l_rate) # Initialize a network def initialize_network(n_inputs, n_hidden, n_outputs): network = list() hidden_layer = [{'weights':[random() for i in range(n_inputs + 1)]} for i in range(n_hidden)] network.append(hidden_layer) output_layer = [{'weights':[random() for i in range(n_hidden + 1)]} for i in range(n_outputs)] network.append(output_layer) return network # Make a prediction with a network def predict(network, row): outputs = forward_propagate(network, row) return outputs.index(max(outputs)) # Backpropagation Algorithm With Stochastic Gradient Descent def back_propagation(train, test, l_rate, n_epoch, n_hidden): n_inputs = len(train[0]) - 1 n_outputs = len(set([row[-1] for row in train])) network = initialize_network(n_inputs, n_hidden, n_outputs) train_network(network, train, l_rate, n_epoch, n_outputs) predictions = list() for row in test: prediction = predict(network, row) predictions.append(prediction) return(predictions) # Test Backprop on Seeds dataset seed(1) # load and prepare data filename = 'seeds_dataset.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # normalize input variables minmax = dataset_minmax(dataset) normalize_dataset(dataset, minmax) # evaluate algorithm n_folds = 5 l_rate = 0.3 n_epoch = 500 n_hidden = 5 scores = evaluate_algorithm(dataset, back_propagation, n_folds, l_rate, n_epoch, n_hidden) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

A network with 5 neurons in the hidden layer and 3 neurons in the output layer was constructed. The network was trained for 500 epochs with a learning rate of 0.3. These parameters were found with a little trial and error, but you may be able to do much better.

Running the example prints the average classification accuracy on each fold as well as the average performance across all folds.

You can see that backpropagation and the chosen configuration achieved a mean classification accuracy of about 93% which is dramatically better than the Zero Rule algorithm that did slightly better than 28% accuracy.

Scores: [92.85714285714286, 92.85714285714286, 97.61904761904762, 92.85714285714286, 90.47619047619048] Mean Accuracy: 93.333%

This section lists extensions to the tutorial that you may wish to explore.

**Tune Algorithm Parameters**. Try larger or smaller networks trained for longer or shorter. See if you can get better performance on the seeds dataset.**Additional Methods**. Experiment with different weight initialization techniques (such as small random numbers) and different transfer functions (such as tanh).**More Layers**. Add support for more hidden layers, trained in just the same way as the one hidden layer used in this tutorial.**Regression**. Change the network so that there is only one neuron in the output layer and that a real value is predicted. Pick a regression dataset to practice on. A linear transfer function could be used for neurons in the output layer, or the output values of the chosen dataset could be scaled to values between 0 and 1.**Batch Gradient Descent**. Change the training procedure from online to batch gradient descent and update the weights only at the end of each epoch.

**Did you try any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement the Backpropagation algorithm from scratch.

Specifically, you learned:

- How to forward propagate an input to calculate a network output.
- How to back propagate error and update network weights.
- How to apply the backpropagation algorithm to a real world dataset.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer.

The post How to Code a Neural Network with Backpropagation In Python (from scratch) appeared first on Machine Learning Mastery.

]]>The post How To Implement Learning Vector Quantization (LVQ) From Scratch With Python appeared first on Machine Learning Mastery.

]]>The Learning Vector Quantization algorithm addresses this by learning a much smaller subset of patterns that best represent the training data.

In this tutorial, you will discover how to implement the Learning Vector Quantization algorithm from scratch with Python.

After completing this tutorial, you will know:

- How to learn a set of codebook vectors from a training data set.
- How to make predictions using learned codebook vectors.
- How to apply Learning Vector Quantization to a real predictive modeling problem.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief introduction to the Learning Vector Quantization algorithm and the Ionosphere classification problem that we will use in this tutorial

The Learning Vector Quantization (LVQ) algorithm is a lot like k-Nearest Neighbors.

Predictions are made by finding the best match among a library of patterns. The difference is that the library of patterns is learned from training data, rather than using the training patterns themselves.

The library of patterns are called codebook vectors and each pattern is called a codebook. The codebook vectors are initialized to randomly selected values from the training dataset. Then, over a number of epochs, they are adapted to best summarize the training data using a learning algorithm.

The learning algorithm shows one training record at a time, finds the best matching unit among the codebook vectors and moves it closer to the training record if they have the same class, or further away if they have different classes.

Once prepared, the codebook vectors are used to make predictions using the k-Nearest Neighbors algorithm where k=1.

The algorithm was developed for classification predictive modeling problems, but can be adapted for use with regression problems.

The Ionosphere dataset predicts the structure of the ionosphere given radar return data.

Each instance describes the properties of radar returns from the atmosphere and the task is to predict whether or not there is structure in the ionosphere.

There are 351 instances and 34 numerical input variables, 17 pairs of 2 for each radar pulse that generally have the same scale of 0-1. The class value is a string with a value of either a “g” for good return or “b” for a bad return.

Using the Zero Rule Algorithm that predicts the class with the most observations, a baseline accuracy of 64.286% can be achieved.

You can learn more and download the dataset from the UCI Machine Learning Repository.

Download the dataset and place it in your current working directory with the name **ionosphere.csv**.

This tutorial is broken down into 4 parts:

- Euclidean Distance.
- Best Matching Unit.
- Training Codebook Vectors.
- Ionosphere Case Study.

These steps will lay the foundation for implementing and applying the LVQ algorithm to your own predictive modeling problems.

The first step needed is to calculate the distance between two rows in a dataset.

Rows of data are mostly made up of numbers and an easy way to calculate the distance between two rows or vectors of numbers is to draw a straight line. This makes sense in 2D or 3D and scales nicely to higher dimensions.

We can calculate the straight line distance between two vectors using the Euclidean distance measure. It is calculated as the square root of the sum of the squared differences between the two vectors.

distance = sqrt( sum( (x1_i - x2_i)^2 )

Where **x1** is the first row of data, **x2** is the second row of data and **i** is the index for a specific column as we sum across all columns.

With Euclidean distance, the smaller the value, the more similar two records will be. A value of 0 means that there is no difference between two records.

Below is a function named **euclidean_distance()** that implements this in Python.

# calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance)

You can see that the function assumes that the last column in each row is an output value which is ignored from the distance calculation.

We can test this distance function with a small contrived classification dataset. We will use this dataset a few times as we construct the elements needed for the LVQ algorithm.

X1 X2 Y 2.7810836 2.550537003 0 1.465489372 2.362125076 0 3.396561688 4.400293529 0 1.38807019 1.850220317 0 3.06407232 3.005305973 0 7.627531214 2.759262235 1 5.332441248 2.088626775 1 6.922596716 1.77106367 1 8.675418651 -0.242068655 1 7.673756466 3.508563011 1

Putting this all together, we can write a small example to test our distance function by printing the distance between the first row and all other rows. We would expect the distance between the first row and itself to be 0, a good thing to look out for.

The full example is listed below.

from math import sqrt # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Test distance function dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] row0 = dataset[0] for row in dataset: distance = euclidean_distance(row0, row) print(distance)

Running this example prints the distances between the first row and every row in the dataset, including itself.

0.0 1.32901739153 1.94946466557 1.55914393855 0.535628072194 4.85094018699 2.59283375995 4.21422704263 6.52240998823 4.98558538245

Now it is time to use the distance calculation to locate the best matching unit within a dataset.

The Best Matching Unit or BMU is the codebook vector that is most similar to a new piece of data.

To locate the BMU for a new piece of data within a dataset we must first calculate the distance between each codebook to the new piece of data. We can do this using our distance function above.

Once distances are calculated, we must sort all of the codebooks by their distance to the new data. We can then return the first or most similar codebook vector.

We can do this by keeping track of the distance for each record in the dataset as a tuple, sort the list of tuples by the distance (in descending order) and then retrieve the BMU.

Below is a function named **get_best_matching_unit()** that implements this.

# Locate the best matching unit def get_best_matching_unit(codebooks, test_row): distances = list() for codebook in codebooks: dist = euclidean_distance(codebook, test_row) distances.append((codebook, dist)) distances.sort(key=lambda tup: tup[1]) return distances[0][0]

You can see that the **euclidean_distance()** function developed in the previous step is used to calculate the distance between each codebook and the new **test_row**.

The list of codebook and distance tuples is sorted where a custom key is used ensuring that the second item in the tuple (**tup[1]**) is used in the sorting operation.

Finally, the top or most similar codebook vector is returned as the BMU.

We can test this function with the small contrived dataset prepared in the previous section.

The complete example is listed below.

from math import sqrt # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the best matching unit def get_best_matching_unit(codebooks, test_row): distances = list() for codebook in codebooks: dist = euclidean_distance(codebook, test_row) distances.append((codebook, dist)) distances.sort(key=lambda tup: tup[1]) return distances[0][0] # Test best matching unit function dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] test_row = dataset[0] bmu = get_best_matching_unit(dataset, test_row) print(bmu)

Running this example prints the BMU in the dataset to the first record. As expected, the first record is the most similar to itself and is at the top of the list.

[2.7810836, 2.550537003, 0]

Make predictions with a set of codebook vectors is the same thing.

We use the 1-nearest neighbor algorithm. That is, for each new pattern we wish to make a prediction for, we locate the most similar codebook vector in the set and return its associated class value.

Now that we know how to get the best matching unit from a set of codebook vectors, we need to learn how to train them.

The first step in training a set of codebook vectors is to initialize the set.

We can initialize it with patterns constructed from random features in the training dataset.

Below is a function named **random_codebook()** that implements this. Random input and output features are selected from the training data.

# Create a random codebook vector def random_codebook(train): n_records = len(train) n_features = len(train[0]) codebook = [train[randrange(n_records)][i] for i in range(n_features)] return codebook

After the codebook vectors are initialized to a random set, they must be adapted to best summarize the training data.

This is done iteratively.

**Epochs**: At the top level, the process is repeated for a fixed number of epochs or exposures of the training data.**Training Dataset**: Within an epoch, each training pattern is used one at a time to update the set of codebook vectors.**Pattern Features**: For a given training pattern, each feature of a best matching codebook vector is updated to move it closer or further away.

The best matching unit is found for each training pattern and only this best matching unit is updated. The difference between the training pattern and the BMU is calculated as the error. The class values (assumed to be the last value in the list) are compared. If they match, the error is added to the BMU to bring it closer to the training pattern, otherwise, it is subtracted to push it further away.

The amount that the BMU is adjusted is controlled by a learning rate. This is a weighting on the amount of change made to all BMUs. For example, a learning rate of 0.3 means that BMUs are only moved by 30% of the error or difference between training patterns and BMUs.

Further, the learning rate is adjusted so that it has maximum effect in the first epoch and less effect as training continues until it has a minimal effect in the final epoch. This is called a linear decay learning rate schedule and can also be used in artificial neural networks.

We can summarize this decay in learning rate by epoch number as follows:

rate = learning_rate * (1.0 - (epoch/total_epochs))

We can test this equation by assuming a learning rate of 0.3 and 10 epochs. The learning rate each epoch would be as follows:

Epoch Effective Learning Rate 0 0.3 1 0.27 2 0.24 3 0.21 4 0.18 5 0.15 6 0.12 7 0.09 8 0.06 9 0.03

We can put all of this together. Below is a function named **train_codebooks()** that implements the procedure for training a set of codebook vectors given a training dataset.

The function takes 3 additional arguments to the training dataset, the number of codebook vectors to create and train, the initial learning rate and the number of epochs for which to train the codebook vectors.

You can also see that the function keeps track of the sum squared error each epoch and prints a message showing the epoch number, effective learning rate and sum squared error score. This is helpful when debugging the training function or the specific configuration for a given prediction problem.

You can see the use of the **random_codebook()** to initialize the codebook vectors and the **get_best_matching_unit()** function to find the BMU for each training pattern within an epoch.

# Train a set of codebook vectors def train_codebooks(train, n_codebooks, lrate, epochs): codebooks = [random_codebook(train) for i in range(n_codebooks)] for epoch in range(epochs): rate = lrate * (1.0-(epoch/float(epochs))) sum_error = 0.0 for row in train: bmu = get_best_matching_unit(codebooks, row) for i in range(len(row)-1): error = row[i] - bmu[i] sum_error += error**2 if bmu[-1] == row[-1]: bmu[i] += rate * error else: bmu[i] -= rate * error print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, rate, sum_error)) return codebooks

We can put this together with the examples above and learn a set of codebook vectors for our contrived dataset.

Below is the complete example.

from math import sqrt from random import randrange from random import seed # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the best matching unit def get_best_matching_unit(codebooks, test_row): distances = list() for codebook in codebooks: dist = euclidean_distance(codebook, test_row) distances.append((codebook, dist)) distances.sort(key=lambda tup: tup[1]) return distances[0][0] # Create a random codebook vector def random_codebook(train): n_records = len(train) n_features = len(train[0]) codebook = [train[randrange(n_records)][i] for i in range(n_features)] return codebook # Train a set of codebook vectors def train_codebooks(train, n_codebooks, lrate, epochs): codebooks = [random_codebook(train) for i in range(n_codebooks)] for epoch in range(epochs): rate = lrate * (1.0-(epoch/float(epochs))) sum_error = 0.0 for row in train: bmu = get_best_matching_unit(codebooks, row) for i in range(len(row)-1): error = row[i] - bmu[i] sum_error += error**2 if bmu[-1] == row[-1]: bmu[i] += rate * error else: bmu[i] -= rate * error print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, rate, sum_error)) return codebooks # Test the training function seed(1) dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] learn_rate = 0.3 n_epochs = 10 n_codebooks = 2 codebooks = train_codebooks(dataset, n_codebooks, learn_rate, n_epochs) print('Codebooks: %s' % codebooks)

Running this example trains a set of 2 codebook vectors for 10 epochs with an initial learning rate of 0.3. The details are printed each epoch and the set of 2 codebook vectors learned from the training data is displayed.

We can see that the changes to learning rate meet our expectations explored above for each epoch. We can also see that the sum squared error each epoch does continue to drop at the end of training and that there may be an opportunity to tune the example further to achieve less error.

>epoch=0, lrate=0.300, error=43.270 >epoch=1, lrate=0.270, error=30.403 >epoch=2, lrate=0.240, error=27.146 >epoch=3, lrate=0.210, error=26.301 >epoch=4, lrate=0.180, error=25.537 >epoch=5, lrate=0.150, error=24.789 >epoch=6, lrate=0.120, error=24.058 >epoch=7, lrate=0.090, error=23.346 >epoch=8, lrate=0.060, error=22.654 >epoch=9, lrate=0.030, error=21.982 Codebooks: [[2.432316086217663, 2.839821664184211, 0], [7.319592257892681, 1.97013382654341, 1]]

Now that we know how to train a set of codebook vectors, let’s see how we can use this algorithm on a real dataset.

In this section, we will apply the Learning Vector Quantization algorithm to the Ionosphere dataset.

The first step is to load the dataset and convert the loaded data to numbers that we can use with the Euclidean distance calculation. For this we will use the helper function **load_csv()** to load the file, **str_column_to_float()** to convert string numbers to floats and **str_column_to_int()** to convert the class column to integer values.

We will evaluate the algorithm using k-fold cross-validation with 5 folds. This means that 351/5=70.2 or just over 70 records will be in each fold. We will use the helper functions **evaluate_algorithm()** to evaluate the algorithm with cross-validation and **accuracy_metric()** to calculate the accuracy of predictions.

The complete example is listed below.

# LVQ for the Ionosphere Dataset from random import seed from random import randrange from csv import reader from math import sqrt # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # calculate the Euclidean distance between two vectors def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] - row2[i])**2 return sqrt(distance) # Locate the best matching unit def get_best_matching_unit(codebooks, test_row): distances = list() for codebook in codebooks: dist = euclidean_distance(codebook, test_row) distances.append((codebook, dist)) distances.sort(key=lambda tup: tup[1]) return distances[0][0] # Make a prediction with codebook vectors def predict(codebooks, test_row): bmu = get_best_matching_unit(codebooks, test_row) return bmu[-1] # Create a random codebook vector def random_codebook(train): n_records = len(train) n_features = len(train[0]) codebook = [train[randrange(n_records)][i] for i in range(n_features)] return codebook # Train a set of codebook vectors def train_codebooks(train, n_codebooks, lrate, epochs): codebooks = [random_codebook(train) for i in range(n_codebooks)] for epoch in range(epochs): rate = lrate * (1.0-(epoch/float(epochs))) for row in train: bmu = get_best_matching_unit(codebooks, row) for i in range(len(row)-1): error = row[i] - bmu[i] if bmu[-1] == row[-1]: bmu[i] += rate * error else: bmu[i] -= rate * error return codebooks # LVQ Algorithm def learning_vector_quantization(train, test, n_codebooks, lrate, epochs): codebooks = train_codebooks(train, n_codebooks, lrate, epochs) predictions = list() for row in test: output = predict(codebooks, row) predictions.append(output) return(predictions) # Test LVQ on Ionosphere dataset seed(1) # load and prepare data filename = 'ionosphere.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert class column to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 5 learn_rate = 0.3 n_epochs = 50 n_codebooks = 20 scores = evaluate_algorithm(dataset, learning_vector_quantization, n_folds, n_codebooks, learn_rate, n_epochs) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

Running this example prints the classification accuracy on each fold and the mean classification accuracy across all folds.

We can see that the accuracy of 87.143% is better than the baseline of 64.286%. We can also see that our library of 20 codebook vectors is far fewer than holding the entire training dataset.

Scores: [90.0, 88.57142857142857, 84.28571428571429, 87.14285714285714, 85.71428571428571] Mean Accuracy: 87.143%

This section lists extensions to the tutorial that you may wish to explore.

**Tune Parameters**. The parameters in the above example were not tuned, try different values to improve the classification accuracy.**Different Distance Measures**. Experiment with different distance measures such as Manhattan distance and Minkowski distance.**Multiple-Pass LVQ**. The codebook vectors may be updated by multiple training runs. Experiment by training with large learning rates followed by a large number of epochs with smaller learning rates to fine tune the codebooks.**Update More BMUs**. Experiment with selecting more than one BMU when training and pushing and pulling them away from the training data.**More Problems**. Apply LVQ to more classification problems on the UCI Machine Learning Repository.

**Did you explore any of these extensions?**

Share your experiences in the comments below.

In this tutorial, you discovered how to implement the learning vector quantization algorithm from scratch in Python.

Specifically, you learned:

- How to calculate the distance between patterns and locate the best matching unit.
- How to train a set of codebook vectors to best summarize the training dataset.
- How to apply the learning vector quantization algorithm to a real predictive modeling problem.

**Do you have any questions?**

Ask your questions in the comments below and I will do my best to answer.

The post How To Implement Learning Vector Quantization (LVQ) From Scratch With Python appeared first on Machine Learning Mastery.

]]>The post How To Implement The Perceptron Algorithm From Scratch In Python appeared first on Machine Learning Mastery.

]]>It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks.

In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python.

After completing this tutorial, you will know:

- How to train the network weights for the Perceptron.
- How to make predictions with the Perceptron.
- How to implement the Perceptron algorithm for a real-world classification problem.

Let’s get started.

**Update Jan/2017**: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.**Update Aug/2018**: Tested and updated to work with Python 3.6.

This section provides a brief introduction to the Perceptron algorithm and the Sonar dataset to which we will later apply it.

The Perceptron is inspired by the information processing of a single neural cell called a neuron.

A neuron accepts input signals via its dendrites, which pass the electrical signal down to the cell body.

In a similar way, the Perceptron receives input signals from examples of training data that we weight and combined in a linear equation called the activation.

activation = sum(weight_i * x_i) + bias

The activation is then transformed into an output value or prediction using a transfer function, such as the step transfer function.

prediction = 1.0 if activation >= 0.0 else 0.0

In this way, the Perceptron is a classification algorithm for problems with two classes (0 and 1) where a linear equation (like or hyperplane) can be used to separate the two classes.

It is closely related to linear regression and logistic regression that make predictions in a similar way (e.g. a weighted sum of inputs).

The weights of the Perceptron algorithm must be estimated from your training data using stochastic gradient descent.

Gradient Descent is the process of minimizing a function by following the gradients of the cost function.

This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value.

In machine learning, we can use a technique that evaluates and updates the weights every iteration called stochastic gradient descent to minimize the error of a model on our training data.

The way this optimization algorithm works is that each training instance is shown to the model one at a time. The model makes a prediction for a training instance, the error is calculated and the model is updated in order to reduce the error for the next prediction.

This procedure can be used to find the set of weights in a model that result in the smallest error for the model on the training data.

For the Perceptron algorithm, each iteration the weights (**w**) are updated using the equation:

w = w + learning_rate * (expected - predicted) * x

Where **w** is weight being optimized, **learning_rate** is a learning rate that you must configure (e.g. 0.01), **(expected – predicted)** is the prediction error for the model on the training data attributed to the weight and **x** is the input value.

The dataset we will use in this tutorial is the Sonar dataset.

This is a dataset that describes sonar chirp returns bouncing off different services. The 60 input variables are the strength of the returns at different angles. It is a binary classification problem that requires a model to differentiate rocks from metal cylinders.

It is a well-understood dataset. All of the variables are continuous and generally in the range of 0 to 1. As such we will not have to normalize the input data, which is often a good practice with the Perceptron algorithm. The output variable is a string “M” for mine and “R” for rock, which will need to be converted to integers 1 and 0.

By predicting the class with the most observations in the dataset (M or mines) the Zero Rule Algorithm can achieve an accuracy of 53%.

You can learn more about this dataset at the UCI Machine Learning repository. You can download the dataset for free and place it in your working directory with the filename **sonar.all-data.csv**.

This tutorial is broken down into 3 parts:

- Making Predictions.
- Training Network Weights.
- Modeling the Sonar Dataset.

These steps will give you the foundation to implement and apply the Perceptron algorithm to your own classification predictive modeling problems.

The first step is to develop a function that can make predictions.

This will be needed both in the evaluation of candidate weights values in stochastic gradient descent, and after the model is finalized and we wish to start making predictions on test data or new data.

Below is a function named **predict()** that predicts an output value for a row given a set of weights.

The first weight is always the bias as it is standalone and not responsible for a specific input value.

# Make a prediction with weights def predict(row, weights): activation = weights[0] for i in range(len(row)-1): activation += weights[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0

We can contrive a small dataset to test our prediction function.

We can also use previously prepared weights to make predictions for this dataset.

Putting this all together we can test our **predict()** function below.

# Make a prediction with weights def predict(row, weights): activation = weights[0] for i in range(len(row)-1): activation += weights[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # test predictions dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] weights = [-0.1, 0.20653640140000007, -0.23418117710000003] for row in dataset: prediction = predict(row, weights) print("Expected=%d, Predicted=%d" % (row[-1], prediction))

There are two inputs values (**X1** and **X2**) and three weight values (**bias**, **w1** and **w2**). The activation equation we have modeled for this problem is:

activation = (w1 * X1) + (w2 * X2) + bias

Or, with the specific weight values we chose by hand as:

activation = (0.206 * X1) + (-0.234 * X2) + -0.1

Running this function we get predictions that match the expected output (**y**) values.

Expected=0, Predicted=0 Expected=0, Predicted=0 Expected=0, Predicted=0 Expected=0, Predicted=0 Expected=0, Predicted=0 Expected=1, Predicted=1 Expected=1, Predicted=1 Expected=1, Predicted=1 Expected=1, Predicted=1 Expected=1, Predicted=1

Now we are ready to implement stochastic gradient descent to optimize our weight values.

We can estimate the weight values for our training data using stochastic gradient descent.

Stochastic gradient descent requires two parameters:

**Learning Rate**: Used to limit the amount each weight is corrected each time it is updated.**Epochs**: The number of times to run through the training data while updating the weight.

These, along with the training data will be the arguments to the function.

There are 3 loops we need to perform in the function:

- Loop over each epoch.
- Loop over each row in the training data for an epoch.
- Loop over each weight and update it for a row in an epoch.

As you can see, we update each weight for each row in the training data, each epoch.

Weights are updated based on the error the model made. The error is calculated as the difference between the expected output value and the prediction made with the candidate weights.

There is one weight for each input attribute, and these are updated in a consistent way, for example:

w(t+1)= w(t) + learning_rate * (expected(t) - predicted(t)) * x(t)

The bias is updated in a similar way, except without an input as it is not associated with a specific input value:

bias(t+1) = bias(t) + learning_rate * (expected(t) - predicted(t))

Now we can put all of this together. Below is a function named **train_weights()** that calculates weight values for a training dataset using stochastic gradient descent.

# Estimate Perceptron weights using stochastic gradient descent def train_weights(train, l_rate, n_epoch): weights = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): sum_error = 0.0 for row in train: prediction = predict(row, weights) error = row[-1] - prediction sum_error += error**2 weights[0] = weights[0] + l_rate * error for i in range(len(row)-1): weights[i + 1] = weights[i + 1] + l_rate * error * row[i] print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)) return weights

You can see that we also keep track of the sum of the squared error (a positive value) each epoch so that we can print out a nice message each outer loop.

We can test this function on the same small contrived dataset from above.

# Make a prediction with weights def predict(row, weights): activation = weights[0] for i in range(len(row)-1): activation += weights[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # Estimate Perceptron weights using stochastic gradient descent def train_weights(train, l_rate, n_epoch): weights = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): sum_error = 0.0 for row in train: prediction = predict(row, weights) error = row[-1] - prediction sum_error += error**2 weights[0] = weights[0] + l_rate * error for i in range(len(row)-1): weights[i + 1] = weights[i + 1] + l_rate * error * row[i] print('>epoch=%d, lrate=%.3f, error=%.3f' % (epoch, l_rate, sum_error)) return weights # Calculate weights dataset = [[2.7810836,2.550537003,0], [1.465489372,2.362125076,0], [3.396561688,4.400293529,0], [1.38807019,1.850220317,0], [3.06407232,3.005305973,0], [7.627531214,2.759262235,1], [5.332441248,2.088626775,1], [6.922596716,1.77106367,1], [8.675418651,-0.242068655,1], [7.673756466,3.508563011,1]] l_rate = 0.1 n_epoch = 5 weights = train_weights(dataset, l_rate, n_epoch) print(weights)

We use a learning rate of 0.1 and train the model for only 5 epochs, or 5 exposures of the weights to the entire training dataset.

Running the example prints a message each epoch with the sum squared error for that epoch and the final set of weights.

>epoch=0, lrate=0.100, error=2.000 >epoch=1, lrate=0.100, error=1.000 >epoch=2, lrate=0.100, error=0.000 >epoch=3, lrate=0.100, error=0.000 >epoch=4, lrate=0.100, error=0.000 [-0.1, 0.20653640140000007, -0.23418117710000003]

You can see how the problem is learned very quickly by the algorithm.

Now, let’s apply this algorithm on a real dataset.

In this section, we will train a Perceptron model using stochastic gradient descent on the Sonar dataset.

The example assumes that a CSV copy of the dataset is in the current working directory with the file name **sonar.all-data.csv**.

The dataset is first loaded, the string values converted to numeric and the output column is converted from strings to the integer values of 0 to 1. This is achieved with helper functions **load_csv()**, **str_column_to_float()** and **str_column_to_int()** to load and prepare the dataset.

We will use k-fold cross validation to estimate the performance of the learned model on unseen data. This means that we will construct and evaluate k models and estimate the performance as the mean model error. Classification accuracy will be used to evaluate each model. These behaviors are provided in the **cross_validation_split()**, **accuracy_metric()** and **evaluate_algorithm()** helper functions.

We will use the **predict() and** **train_weights()** functions created above to train the model and a new **perceptron()** function to tie them together.

Below is the complete example.

# Perceptron Algorithm on the Sonar Dataset from random import seed from random import randrange from csv import reader # Load a CSV file def load_csv(filename): dataset = list() with open(filename, 'r') as file: csv_reader = reader(file) for row in csv_reader: if not row: continue dataset.append(row) return dataset # Convert string column to float def str_column_to_float(dataset, column): for row in dataset: row[column] = float(row[column].strip()) # Convert string column to integer def str_column_to_int(dataset, column): class_values = [row[column] for row in dataset] unique = set(class_values) lookup = dict() for i, value in enumerate(unique): lookup[value] = i for row in dataset: row[column] = lookup[row[column]] return lookup # Split a dataset into k folds def cross_validation_split(dataset, n_folds): dataset_split = list() dataset_copy = list(dataset) fold_size = int(len(dataset) / n_folds) for i in range(n_folds): fold = list() while len(fold) < fold_size: index = randrange(len(dataset_copy)) fold.append(dataset_copy.pop(index)) dataset_split.append(fold) return dataset_split # Calculate accuracy percentage def accuracy_metric(actual, predicted): correct = 0 for i in range(len(actual)): if actual[i] == predicted[i]: correct += 1 return correct / float(len(actual)) * 100.0 # Evaluate an algorithm using a cross validation split def evaluate_algorithm(dataset, algorithm, n_folds, *args): folds = cross_validation_split(dataset, n_folds) scores = list() for fold in folds: train_set = list(folds) train_set.remove(fold) train_set = sum(train_set, []) test_set = list() for row in fold: row_copy = list(row) test_set.append(row_copy) row_copy[-1] = None predicted = algorithm(train_set, test_set, *args) actual = [row[-1] for row in fold] accuracy = accuracy_metric(actual, predicted) scores.append(accuracy) return scores # Make a prediction with weights def predict(row, weights): activation = weights[0] for i in range(len(row)-1): activation += weights[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # Estimate Perceptron weights using stochastic gradient descent def train_weights(train, l_rate, n_epoch): weights = [0.0 for i in range(len(train[0]))] for epoch in range(n_epoch): for row in train: prediction = predict(row, weights) error = row[-1] - prediction weights[0] = weights[0] + l_rate * error for i in range(len(row)-1): weights[i + 1] = weights[i + 1] + l_rate * error * row[i] return weights # Perceptron Algorithm With Stochastic Gradient Descent def perceptron(train, test, l_rate, n_epoch): predictions = list() weights = train_weights(train, l_rate, n_epoch) for row in test: prediction = predict(row, weights) predictions.append(prediction) return(predictions) # Test the Perceptron algorithm on the sonar dataset seed(1) # load and prepare data filename = 'sonar.all-data.csv' dataset = load_csv(filename) for i in range(len(dataset[0])-1): str_column_to_float(dataset, i) # convert string class to integers str_column_to_int(dataset, len(dataset[0])-1) # evaluate algorithm n_folds = 3 l_rate = 0.01 n_epoch = 500 scores = evaluate_algorithm(dataset, perceptron, n_folds, l_rate, n_epoch) print('Scores: %s' % scores) print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))

A k value of 3 was used for cross-validation, giving each fold 208/3 = 69.3 or just under 70 records to be evaluated upon each iteration. A learning rate of 0.1 and 500 training epochs were chosen with a little experimentation.

You can try your own configurations and see if you can beat my score.

Running this example prints the scores for each of the 3 cross-validation folds then prints the mean classification accuracy.

We can see that the accuracy is about 72%, higher than the baseline value of just over 50% if we only predicted the majority class using the Zero Rule Algorithm.

Scores: [76.81159420289855, 69.56521739130434, 72.46376811594203] Mean Accuracy: 72.947%

This section lists extensions to this tutorial that you may wish to consider exploring.

**Tune The Example**. Tune the learning rate, number of epochs and even data preparation method to get an improved score on the dataset.**Batch Stochastic Gradient Descent**. Change the stochastic gradient descent algorithm to accumulate updates across each epoch and only update the weights in a batch at the end of the epoch.**Additional Regression Problems**. Apply the technique to other classification problems on the UCI machine learning repository.

**Did you explore any of these extensions?**

Let me know about it in the comments below.

In this tutorial, you discovered how to implement the Perceptron algorithm using stochastic gradient descent from scratch with Python.

You learned.

- How to make predictions for a binary classification problem.
- How to optimize a set of weights using stochastic gradient descent.
- How to apply the technique to a real classification predictive modeling problem.

**Do you have any questions?**

Ask your question in the comments below and I will do my best to answer.

The post How To Implement The Perceptron Algorithm From Scratch In Python appeared first on Machine Learning Mastery.

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