You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
3.8 KiB
128 lines
3.8 KiB
import csv
|
|
import sys
|
|
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
|
|
passv = lambda x: x
|
|
MONTHS = ['Jan', 'Feb', 'Mar', 'April', 'May', 'June', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
|
|
|
|
|
|
TEST_SIZE = 0.4
|
|
CONVERSION_FUNCTIONS = [int, float, int, float, int, float, float, float,
|
|
float, float, MONTHS.index , int, int, int, int, lambda x: 1 if x == "Returning_Visitor" else 0,
|
|
lambda x: 1 if x!="FALSE" else 0]
|
|
|
|
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.
|
|
"""
|
|
data = []
|
|
labels = []
|
|
with open("shopping.csv", "r") as f:
|
|
reader = csv.reader(f)
|
|
first = True
|
|
for line in reader:
|
|
if first:
|
|
first = False
|
|
continue
|
|
row = list(line)
|
|
data.append([None] * (len(row) - 1))
|
|
for i in range(len(row) - 1):
|
|
data[-1][i] = CONVERSION_FUNCTIONS[i](row[i])
|
|
labels.append(1 if row[-1]!="FALSE" else 0)
|
|
return data, 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.
|
|
"""
|
|
model = KNeighborsClassifier(n_neighbors=10)
|
|
model.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_n = 0
|
|
true_p = 0
|
|
negatives = 0
|
|
positives = 0
|
|
for i, j in zip(labels, predictions):
|
|
if i == 0:
|
|
if i == j:
|
|
true_n += 1
|
|
negatives += 1
|
|
else:
|
|
if i == j:
|
|
true_p += 1
|
|
positives += 1
|
|
specificity = true_n / negatives
|
|
sensitivity = true_p / positives
|
|
return sensitivity, specificity
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|