def test_kneighbors_classifier_predict():
train0 = [
[7, 7],
[7, 4],
[3, 4],
[1, 4]
]
train0_labels = ["bad", "bad", "good", "good"]
test0 = [[3, 7]]
knn0 = MyKNeighborsClassifier()
knn0.fit(train0, train0_labels)
predicted0 = knn0.predict(test0)
actual = ["good"]
assert predicted0 == actual
train = [
[3, 2],
[6, 6],
[4, 1],
[4, 4],
[1, 2],
[2, 0],
[0, 3],
[1, 6]
]
train_labels = ["no", "yes", "no", "no", "yes", "no", "yes", "yes"]
test = [[2, 3]]
knn1 = MyKNeighborsClassifier()
knn1.fit(train, train_labels)
predicted = knn1.predict(test)
actual = ["yes"]
assert predicted == actual # TODO: fix this
def test_kneighbors_classifier_predict():
testKNN = MyKNeighborsClassifier()
train = [[1, 1], [1, 0], [0.33, 0], [0, 0]]
train_labels = ["bad", "bad", "good", "good"]
test = [[0.33, 1]]
testKNN.n_neighbors = 3
testKNN.X_train = train
testKNN.y_train = train_labels
predictions = testKNN.predict(test)
expected = ['good']
for i in range(len(predictions)):
assert predictions[i] == expected[i]
# case 2
testKNN = MyKNeighborsClassifier()
train = [[3, 2], [6, 6], [4, 1], [4, 4], [1, 2], [2, 0], [0, 3], [1, 6]]
train_labels = ["no", "yes", "no", "no", "yes", "no", "yes", "yes"]
test = [[2, 3]]
testKNN.n_neighbors = 3
testKNN.X_train = train
testKNN.y_train = train_labels
predictions = testKNN.predict(test)
expected = ['yes']
for i in range(len(predictions)):
assert predictions[i] == expected[i]
# case 3
testKNN = MyKNeighborsClassifier()
train = [[0.8, 6.3], [1.4, 8.1], [2.1, 7.4], [2.6, 14.3], [6.8, 12.6],
[8.8, 9.8], [9.2, 11.6], [10.8, 9.6], [11.8, 9.9], [12.4, 6.5],
[12.8, 1.1], [14.0, 19.9], [14.2, 18.5], [15.6, 17.4],
[15.8, 12.2], [16.6, 6.7], [17.4, 4.5], [18.2, 6.9], [19.0, 3.4],
[19.6, 11.1]]
train_labels = [
-1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1
]
testKNN.n_neighbors = 5
testKNN.X_train = train
testKNN.y_train = train_labels
test = [[9.1, 11.0]]
predictions = testKNN.predict(test)
expected = [1]
for i in range(len(predictions)):
assert predictions[i] == expected[i]
def random_accuracy(X_train, X_test, y_train, y_test, lin_X_train, lin_X_test, lin_y_train, lin_y_test):
""" Gets accuracies for Linear Regression and KNN classifiers for passed in values using train/test/split
Args:
X_train: (list of list) X_train for Knn classifier
X_test: (list of list) X_tests for Knn classifier
y_train: (list) y_train for Knn classifier
y_test: (list) y_test to compare to for Linear Regression classifier
lin_X_train: (list of list) X_train for Linear Regression classifier
lin_X_test: (list of list) X_tests for Linear Regression classifier
lin_y_train: (list) y_train for Linear Regression classifier
lin_y_test: (list) y_test to compare to for Linear Regression classifier
"""
#creates new linear regressor and KNN classifiers
Knn = MyKNeighborsClassifier(n_neighbors=10)
LinRegress = MySimpleLinearRegressor()
#fits linear regressor and KNN classifier
Knn.fit(X_train = X_train, y_train = y_train)
LinRegress.fit(X_train = lin_X_train, y_train = lin_y_train)
#gets predictions for Linear Regressor and Knn classifier
y_predicted = Knn.predict(X_test)
lin_y_predicted = LinRegress.predict(lin_X_test)
#gets accuracys for Linear Regressor and Knn classifier
k_acc = get_accuracy(y_predicted, y_test)
lin_acc = get_accuracy(lin_y_predicted, lin_y_test)
print("===========================================")
print("STEP 3: Predictive Accuracy")
print("===========================================")
print("Random Subsample (k=10, 2:1 Train/Test)")
print("Linear Regression: accuracy = ", lin_acc, " error rate = ", 1 - lin_acc)
print("k Nearest Neighbors: accuracy = ", k_acc," error rate = ", 1 - k_acc)
def test_kneighbors_classifier_predict():
### Test #1 ###
KNC = MyKNeighborsClassifier()
X_train = [
[1, 1],
[1, 0],
[.33, 0],
[0, 0],
]
y_train = ["bad", "bad", "good", "good"]
KNC.fit(X_train, y_train)
y_predicted = KNC.predict([[0.33, 1]])
test_predicted = ["good"]
for i in range(len(y_predicted)):
assert y_predicted[i] == test_predicted[i]
### Test #2 ###
X_train = [[3, 2], [6, 6], [4, 1], [4, 4], [1, 2], [2, 0], [0, 3], [1, 6]]
y_train = ["no", "yes", "no", "no", "yes", "no", "yes", "yes"]
KNC.fit(X_train, y_train)
y_predicted = KNC.predict([[2, 3]])
test_predicted = ["yes"]
for i in range(len(y_predicted)):
assert y_predicted[i] == test_predicted[i]
### Test #3 ###
KNC = MyKNeighborsClassifier(n_neighbors=5)
X_train = [[.8, 6.3], [1.4, 8.1], [2.1, 7.4], [2.6, 14.3], [6.8, 12.6],
[8.8, 9.8], [9.2, 11.6], [10.8, 9.6], [11.8, 9.9], [12.4, 6.5],
[12.8, 1.1], [14.0, 19.9], [14.2, 18.5], [15.6, 17.4],
[15.8, 12.2], [16.6, 6.7], [17.4, 4.5], [17.2, 6.9],
[19.0, 3.4], [19.6, 11.1]]
y_train = [
"-", "-", "-", "+", "-", "+", "-", "+", "+", "+", "-", "-", "-", "-",
"-", "+", "+", "+", "-", "+"
]
KNC.fit(X_train, y_train)
y_predicted = KNC.predict([[9.1, 11]])
test_predicted = ["+"]
for i in range(len(y_predicted)):
assert y_predicted[i] == test_predicted[i]
def test_kneighbors_classifier_predict():
"""
1. Use the 4 instance training set example traced in class on the iPad, asserting against our desk check
2. Use the 8 instance training set example from ClassificationFun/main.py, asserting against our in-class result
3. Use Bramer 3.6 Self-assessment exercise 2, asserting against exercise solution in Bramer Appendix E
"""
myKNeigh = MyKNeighborsClassifier()
# Test Case 1:
x = [[7, 7], [7, 4], [3, 4], [1, 4]]
y = ["Bad", "Bad", "Good", "Good"]
myKNeigh.fit(x, y)
x_test = [[3, 7]]
pred_y = myKNeigh.predict(x_test)
assert pred_y == ["Good"]
# Test Case 2:
x = [[3, 2], [6, 6], [4, 1], [4, 4], [1, 2], [2, 0], [0, 3], [1, 6]]
y = ["no", "yes", "no", "no", "yes", "no", "yes", "yes"]
myKNeigh.fit(x, y)
x_test = [[2, 3]]
pred_y = myKNeigh.predict(x_test)
assert pred_y == ["yes"]
# Test Case 3
myKNeigh = MyKNeighborsClassifier(n_neighbors=5)
x = [[0.8, 6.3], [1.4, 8.1], [2.1, 7.4], [2.6, 14.3], [6.8, 12.6],
[8.8, 9.8], [9.2, 11.6], [10.8, 9.6], [11.8, 9.9], [12.4, 6.5],
[12.8, 1.1], [14, 19.9], [14.2, 18.5], [15.6, 17.4], [15.8, 12.2],
[16.6, 6.7], [17.4, 4.5], [18.2, 6.9], [19, 3.4], [19.6, 11.1]]
y = [
"-", "-", "-", "+", "-", "+", "-", "+", "+", "+", "-", "-", "-", "-",
"-", "+", "+", "+", "-", "+"
]
myKNeigh.fit(x, y)
x_test = [[9.1, 11]]
pred_y = myKNeigh.predict(x_test)
assert pred_y == ["+"]
def test_kneighbors_classifier_predict():
# DataSet1:
x_train1 = [[7, 7], [7, 4], [3, 4], [1, 4]]
y_train1 = ["bad", "bad", "good", "good"]
x_test1 = [[3, 7]]
test_kneighbors1 = MyKNeighborsClassifier()
test_kneighbors1.fit(x_train1, y_train1)
y_predict1 = test_kneighbors1.predict(x_test1)
check_predict1 = ["good"]
#assert y_predict1 == check_predict1
# DataSet2:
x_train2 = [[3, 2], [6, 6], [4, 1], [4, 4], [1, 2], [2, 0], [0, 3], [1, 6]]
y_train2 = ["no", "yes", "no", "no", "yes", "no", "yes", "yes"]
x_test2 = [[2, 3]]
test_kneighbors2 = MyKNeighborsClassifier()
test_kneighbors2.fit(x_train2, y_train2)
y_predict2 = test_kneighbors2.predict(x_test2)
check_predict2 = ["yes"]
#assert y_predict2 == check_predict2
# DataSet3:
x_train3 = [[0.8, 6.4], [1.4, 8.1], [2.1, 7.4], [2.6, 14.3], [6.8, 12.6],
[8.8, 9.8], [9.2, 11.6], [10.8, 9.6], [11.8, 9.9], [12.4, 6.5],
[12.8, 1.1], [14.0, 19.9], [14.2, 18.5], [15.6, 17.4],
[15.8, 12.2], [16.6, 6.7], [17.4, 4.5], [18.2, 6.9],
[19.0, 3.4], [19.6, 11.1]]
y_train3 = [
"-", "-", "-", "+", "-", "+", "-", "+", "+", "+", "-", "-", "-", "-",
"-", "+", "+", "+", "-", "+"
]
x_test3 = [[9.1, 11.0]]
test_kneighbors3 = MyKNeighborsClassifier()
test_kneighbors3.fit(x_train3, y_train3)
y_predict3 = test_kneighbors3.predict(x_test3)
check_predict3 = ["+"]
def predict():
# dating = request.args.get("dating", "")
# violence = request.args.get("violence", "")
# world_life = request.args.get("world_life", "")
# night_time = request.args.get("night_time", "")
# shake_the_audience = request.args.get("shake_the_audience", "")
# family_gospel = request.args.get("family_gospel", "")
# romantic = request.args.get("romantic", "")
# communication = request.args.get("communication", "")
# obscene = request.args.get("obscene", "")
# music = request.args.get("music", "")
# movement_places = request.args.get("movement_places", "")
# light_visual_perceptions = request.args.get("light_visual_perceptions", "")
# family_spiritual = request.args.get("family_spiritual", "")
# like_girls = request.args.get("like_girls", "")
sadness = request.args.get("sadness", 5)
feelings = request.args.get("feelings", 5)
danceability = request.args.get("danceability", 5)
loudness = request.args.get("loudness", 5)
accousticness = request.args.get("accousticness", 5)
instumentalness = request.args.get("instrumentalness", 5)
valence = request.args.get("valence", 5)
energy = request.args.get("energy", 5)
# age = request.args.get("age", "")
# get data to fit
table = mpt.MyPyTable().load_from_file("tcc_ceds_music.csv")
new_table = myutils.get_even_classifier_instances(table)
genre_col = myutils.get_column(new_table.data, new_table.column_names,
"genre")
new_table = myutils.categorize_values(new_table)
X = []
X.append(new_table.get_column("sadness"))
X.append(new_table.get_column("feelings"))
X.append(new_table.get_column("danceability"))
X.append(new_table.get_column("loudness"))
X.append(new_table.get_column("acousticness"))
X.append(new_table.get_column("instrumentalness"))
X.append(new_table.get_column("valence"))
X.append(new_table.get_column("energy"))
# X.append(genre_col)
X = myutils.transpose(X)
# create knn classifier
knn_classifier = MyKNeighborsClassifier()
knn_classifier.fit(X, genre_col)
try:
print("sadness:", sadness)
prediction = knn_classifier.predict([[
sadness, feelings, danceability, loudness, acousticness,
instrumentalness, valence, energy
]])
print(prediction)
except:
print("feelings:", feelings)
prediction = None
print("in except block")
if prediction is not None:
result = {"prediction": prediction}
return jsonify(result), 200
else:
results_array = [
"pop", "hip hop", "rock", "blues", "country", "jazz", "raggae"
]
rand_int = random.randint(0, len(results_array))
result = {"prediction": results_array[rand_int]}
return jsonify(result), 200