shopping.py

import csv
import sys

from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

TEST_SIZE = 0.4


def main():

    # Check command-line arguments
    if len(sys.argv) != 2:
        sys.exit("Usage: python shopping.py data")

    # Load data from spreadsheet and split into train and test sets
    evidence, labels = load_data(sys.argv[1])
    X_train, X_test, y_train, y_test = train_test_split(
        evidence, labels, test_size=TEST_SIZE
    )

    # Train model and make predictions
    model = train_model(X_train, y_train)
    predictions = model.predict(X_test)
    sensitivity, specificity = evaluate(y_test, predictions)

    # Print results
    print(f"Correct: {(y_test == predictions).sum()}")
    print(f"Incorrect: {(y_test != predictions).sum()}")
    print(f"True Positive Rate: {100 * sensitivity:.2f}%")
    print(f"True Negative Rate: {100 * specificity:.2f}%")


def load_data(filename):
    """
    Load shopping data from a CSV file `filename` and convert into a list of
    evidence lists and a list of labels. Return a tuple (evidence, labels).

    evidence should be a list of lists, where each list contains the
    following values, in order:
        - Administrative, an integer
        - Administrative_Duration, a floating point number
        - Informational, an integer
        - Informational_Duration, a floating point number
        - ProductRelated, an integer
        - ProductRelated_Duration, a floating point number
        - BounceRates, a floating point number
        - ExitRates, a floating point number
        - PageValues, a floating point number
        - SpecialDay, a floating point number
        - Month, an index from 0 (January) to 11 (December)
        - OperatingSystems, an integer
        - Browser, an integer
        - Region, an integer
        - TrafficType, an integer
        - VisitorType, an integer 0 (not returning) or 1 (returning)
        - Weekend, an integer 0 (if false) or 1 (if true)

    labels should be the corresponding list of labels, where each label
    is 1 if Revenue is true, and 0 otherwise.
    """

    # initialise empty evidence and labels lists
    evidence = []
    labels = []

    # create a dictionary to map a month to a numeric value between 0 and 11
    months = {"Jan":0, "Feb":1, "Mar":2, "Apr":3, "May":4, "June":5, "Jul":6, "Aug":7, "Sep":8, "Oct": 9, "Nov": 10, "Dec":11}

    # open a file and read it row by row
    with open(filename, 'r') as file:
        reader = csv.DictReader(file)

        for row in reader:
            # append the evidence values into the evidence list in the correct format
            evidence.append([
                int(row["Administrative"]),
                float(row["Administrative_Duration"]),
                int(row["Informational"]),
                float(row["Informational_Duration"]),
                int(row["ProductRelated"]),
                float(row["ProductRelated_Duration"]),
                float(row["BounceRates"]),
                float(row["ExitRates"]),
                float(row["PageValues"]),
                float(row["SpecialDay"]),
                months[row["Month"]],
                int(row["OperatingSystems"]),
                int(row["Browser"]),
                int(row["Region"]),
                int(row["TrafficType"]),
                1 if row["VisitorType"] == "Returning_Visitor" else 0,
                1 if row["Weekend"] == "TRUE" else 0
            ])

            # append the labels into the labels list
            labels.append(1 if row["Revenue"] == "TRUE" else 0)

    # return the lists as tuples
    return(evidence, labels)

def train_model(evidence, labels):
    """
    Given a list of evidence lists and a list of labels, return a
    fitted k-nearest neighbor model (k=1) trained on the data.
    """
    # initialise a k-nearest model with k=1 using scikit 
    model = KNeighborsClassifier(n_neighbors=1).fit(evidence, labels)
    return model


def evaluate(labels, predictions):
    """
    Given a list of actual labels and a list of predicted labels,
    return a tuple (sensitivity, specificty).

    Assume each label is either a 1 (positive) or 0 (negative).

    `sensitivity` should be a floating-point value from 0 to 1
    representing the "true positive rate": the proportion of
    actual positive labels that were accurately identified.

    `specificity` should be a floating-point value from 0 to 1
    representing the "true negative rate": the proportion of
    actual negative labels that were accurately identified.
    """
    true_positive = 0
    positive_count = 0
    true_negative = 0
    negative_count = 0

    for i in range(len(labels)):
        if labels[i] == 1:
            positive_count += 1
            if predictions[i] == 1:
                true_positive += 1

        else:
            negative_count += 1
            if predictions[i] == 0:
                true_negative += 1

    sensitivity = true_positive / positive_count
    specificity = true_negative / negative_count

    return (sensitivity, specificity)

if __name__ == "__main__":
    main()