def test_decision_tree_classifier_predict():
interview_classifier = MyRandomForestClassifier()
interview_classifier.fit(interview_table, interview_class_train, 2, 20, 7,
3)
assert interview_classifier.predict([["Mid", "Java", "yes", "no"],
["Junior", "Python", "no",
"yes"]]) == ["True", "False"]
def test_My_Random_Forest_Classifier_predict():
# Object Declarations
# Tests with N = 3, M = 2, F = 2 and seed = 1
rand_forest_test = MyRandomForestClassifier(3, 2, 2, 1)
table = MyPyTable()
# Variable Assignment and Declaration
table.data = interview_table
table.column_names = interview_header
y_train, X_train = [], []
for inst in interview_table:
y_train.append(inst[-1])
X_train.append(inst[:-1])
# Sets X_test
X_test = [["Junior", "Java", "yes", "no"],
["Junior", "Java", "yes", "yes"]]
# Tests on the Interview Dataset
rand_forest_test.header = interview_header[:-1]
rand_forest_test.fit(X_train, y_train)
y_predicted = rand_forest_test.predict(X_test)
print("y_predicted:", y_predicted)
# Trace Test
assert y_predicted == ['True', 'False']
def tune_parameters(M, N, F, dataset):
print("M =", M, "N =", N, "F =", F)
adjusted_dataset = select_random_attributes(F, dataset.data)
for i in range(5):
X, y = split_x_y_train(adjusted_dataset)
x_train, x_test, y_train, y_test = myevaluation.train_test_split(
X, y, shuffle=True)
remainder = []
for j in range(len(x_train)):
row = x_train[j]
row.append(y_train[j])
remainder.append(row)
myRF = MyRandomForestClassifier()
myRF.fit(remainder, M, N)
y_predict_rf = myRF.predict(x_test)
count = 0
for l in range(len(y_predict_rf)):
binned_predict = get_useful_bin(y_predict_rf[l])
binned_test = get_useful_bin(y_test[l])
if (binned_predict == binned_test):
count = count + 1
accuracy = count / len(y_predict_rf)
error = (len(y_predict_rf) - count) / len(y_predict_rf)
print(i, "-- accuracy =", accuracy, "error =", error)
def test_random_forest_fit():
interview_header = ["level", "lang", "tweets", "phd", "interviewed_well"]
interview_table = [["Senior", "Java", "no", "no", "False"],
["Senior", "Java", "no", "yes", "False"],
["Mid", "Python", "no", "no", "True"],
["Junior", "Python", "no", "no", "True"],
["Junior", "R", "yes", "no", "True"],
["Junior", "R", "yes", "yes", "False"],
["Mid", "R", "yes", "yes", "True"],
["Senior", "Python", "no", "no", "False"],
["Senior", "R", "yes", "no", "True"],
["Junior", "Python", "yes", "no", "True"],
["Senior", "Python", "yes", "yes", "True"],
["Mid", "Python", "no", "yes", "True"],
["Mid", "Java", "yes", "no", "True"],
["Junior", "Python", "no", "yes", "False"]]
myutils.prepend_attribute_label(interview_table, interview_header)
interview_pytable = MyPyTable(column_names=interview_header,
data=interview_table)
y_col = interview_pytable.get_column("interviewed_well", False)
x_cols = interview_pytable.drop_col("interviewed_well")
many_trees = MyRandomForestClassifier()
X_sample, y_sample = myutils.compute_bootstrapped_sample(x_cols, y_col)
X_train, X_test, y_train, y_test = myutils.train_test_split(
X_sample, y_sample, .33)
many_trees.fit(X_train, y_train, X_test, y_test)
y_predicted = many_trees.predict(X_test)
numCorrectPredictions = 0
numWrongPredictions = 0
for i in range(len(y_test)):
values = [y_predicted[i], y_test[i]] #predicted/actual
if (values[0] == values[1]):
numCorrectPredictions = numCorrectPredictions + 1
else:
numWrongPredictions = numWrongPredictions + 1
accuracy = np.round((numCorrectPredictions) /
(numCorrectPredictions + numWrongPredictions), 3)
error_rate = np.round(
(numWrongPredictions) / (numCorrectPredictions + numWrongPredictions),
3)
print("-----------------------------------------------------------")
print("Accuracy and Error Rate")
print("-----------------------------------------------------------")
print()
print("Random Forest: accuracy = {}, error rate = {}".format(
accuracy, error_rate))
print()
print(
"Because of the random aspect of this classifier, this will not always pass the tests"
)
print()
print("Predicted table: " + str(y_predicted))
print("Testing set: " + str(y_test))
for i in range(len(y_test)):
assert y_predicted[i] == y_test[i]
def test_MyRandomForestClassifier_predict():
random.seed(1)
# Interview DataSet
# Create X_train and y_train
X_train = []
y_train = []
X_test = [["Junior", "R", "yes", "no"], ["Junior", "Python", "no", "yes"],
["Senior", "Java", "no", "no", "False"]]
# Append the header
X_train.append(["level", "lang", "tweets", "phd", "interviewed_well"])
# Delete the classifier
del X_train[0][-1]
# Get X_train
for row in range(len(interview_table)):
tmp = []
for col in range(len(interview_table[0]) - 1):
tmp.append(interview_table[row][col])
X_train.append(tmp)
# Get y_train
for row in range(len(interview_table)):
y_train.append(interview_table[row][-1])
# Create a MyDecisionTreeClassifier object
#print(X_train)
test_fit = MyRandomForestClassifier(100, 2, 2)
# Call fit
actual = ['True', 'True', 'True']
test_fit.fit(X_train, y_train)
predicted = test_fit.predict(X_test)
assert predicted == actual
def test_random_forest_classifier_fit():
# interview dataset
interview_header = ["level", "lang", "tweets", "phd", "interviewed_well"]
interview_table = [["Senior", "Java", "no", "no"],
["Senior", "Java", "no", "yes"],
["Mid", "Python", "no", "no"],
["Junior", "Python", "no", "no"],
["Junior", "R", "yes", "no"],
["Junior", "R", "yes", "yes"],
["Mid", "R", "yes", "yes"],
["Senior", "Python", "no", "no"],
["Senior", "R", "yes", "no"],
["Junior", "Python", "yes", "no"],
["Senior", "Python", "yes", "yes"],
["Mid", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"],
["Junior", "Python", "no", "yes"]]
interview_labels = [
"False", "False", "True", "True", "True", "False", "True", "False",
"True", "True", "True", "True", "True", "False"
]
rfc = MyRandomForestClassifier(20, 7, 2, None)
rfc.fit(interview_table, interview_labels)
assert len(rfc.learners) == 7
assert len(rfc.accuracies) == 7
def test_my_random_forest_fit():
interview_classifier = MyRandomForestClassifier()
interview_classifier.fit(interview_table, interview_class_train, 2, 20, 7,
3)
forest = [[
'Attribute', 'att2', ['Value', 'no', ['Leaf', 'True', 0, 7]],
['Value', 'yes', ['Leaf', 'True', 2, 9]]
], ['Leaf', 'True', 0, 9],
[
'Attribute', 'att0',
[
'Value', 'Junior',
[
'Attribute', 'att3',
['Value', 'no', ['Leaf', 'True', 2, 4]],
['Value', 'yes', ['Leaf', 'False', 2, 4]]
]
], ['Value', 'Mid', ['Leaf', 'True', 4, 9]],
['Value', 'Senior', ['Leaf', 'False', 1, 9]]
],
[
'Attribute', 'att0',
[
'Value', 'Junior',
[
'Attribute', 'att3',
['Value', 'no', ['Leaf', 'True', 2, 4]],
['Value', 'yes', ['Leaf', 'False', 2, 4]]
]
], ['Value', 'Mid', ['Leaf', 'True', 4, 9]],
['Value', 'Senior', ['Leaf', 'False', 1, 9]]
],
[
'Attribute', 'att0',
[
'Value', 'Junior',
[
'Attribute', 'att3',
['Value', 'no', ['Leaf', 'True', 2, 4]],
['Value', 'yes', ['Leaf', 'False', 2, 4]]
]
], ['Value', 'Mid', ['Leaf', 'True', 4, 9]],
['Value', 'Senior', ['Leaf', 'False', 1, 9]]
], ['Leaf', 'True', 0, 9],
[
'Attribute', 'att0',
[
'Value', 'Junior',
[
'Attribute', 'att3',
['Value', 'no', ['Leaf', 'True', 2, 4]],
['Value', 'yes', ['Leaf', 'False', 2, 4]]
]
], ['Value', 'Mid', ['Leaf', 'True', 4, 9]],
['Value', 'Senior', ['Leaf', 'False', 1, 9]]
]]
assert [i for i in interview_classifier.forest if i not in forest] == []
def test_random_forest():
rf = MyRandomForestClassifier()
rf.fit(iphone_x, iphone_y, M=7, N=20, F=2)
assert len(rf.trees) == 7
rf = MyRandomForestClassifier()
rf.fit(iphone_x, iphone_y, M=6, N=20, F=2)
assert len(rf.trees) == 6
def test_random_forest_classifier_fit():
mp_table = MyPyTable(interview_header, interview_table)
# Formulate X_train and y_train
y_train = mp_table.get_column('interviewed_well')
X_train_col_names = ["level", "lang", "tweets", "phd"]
X_train = mp_table.get_rows(X_train_col_names)
myRF = MyRandomForestClassifier(N=4, M=2, F=4)
myRF.fit(X_train, y_train)
assert len(myRF.M_attr_sets) == myRF.M
def test_random_forest_fit():
X = [["Senior", "Java", "no", "no"], ["Senior", "Java", "no", "yes"],
["Mid", "Python", "no", "no"], ["Junior", "Python", "no", "no"],
["Junior", "R", "yes", "no"], ["Junior", "R", "yes", "yes"],
["Mid", "R", "yes", "yes"], ["Senior", "Python", "no", "no"],
["Senior", "R", "yes", "no"], ["Junior", "Python", "yes", "no"],
["Senior", "Python", "yes", "yes"], ["Mid", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"], ["Junior", "Python", "no", "yes"]]
y = [
"False", "False", "True", "True", "True", "False", "True", "False",
"True", "True", "True", "True", "True", "False"
]
test_trees = [[
'Attribute', 'att0',
[
'Value', 'Junior',
[
'Attribute', 'att2', ['Value', 'no', ['Leaf', 'False']],
[
'Value', 'yes',
[
'Attribute', 'att3', ['Value', 'no', ['Leaf', 'True']],
['Value', 'yes', ['Leaf', 'False']]
]
]
]
], ['Value', 'Mid', ['Leaf', 'True']],
[
'Value', 'Senior',
[
'Attribute', 'att2', ['Value', 'no', ['Leaf', 'False']],
['Value', 'yes', ['Leaf', 'True']]
]
]
],
[
'Attribute', 'att0',
['Value', 'Junior', ['Leaf', 'False']],
['Value', 'Mid', ['Leaf', 'True']],
[
'Value', 'Senior',
[
'Attribute', 'att2',
['Value', 'no', ['Leaf', 'False']],
['Value', 'yes', ['Leaf', 'True']]
]
]
]]
forest = MyRandomForestClassifier(n=4, m=2, f=2, seed=2)
forest.fit(X, y)
assert forest.trees == test_trees
def test_My_Random_Forest_Classifier_fit():
# Object Declarations
# Tests with N = 3, M = 2, F = 2 and seed = 0
rand_forest_test = MyRandomForestClassifier(3, 2, 2, 0)
table = MyPyTable()
# Variable Assignment and Declaration
table.data = interview_table
table.column_names = interview_header
X_test = interview_table
y_train = table.get_column("interviewed_well")
# Tests on the Interview Dataset
rand_forest_test.header = interview_header
rand_forest_test.fit(X_test, y_train)
trees = rand_forest_test.trees
def test_random_forest_classifier_predict():
X_test = [["Mid", "Python", "no", "no", "True"],
["Mid", "R", "yes", "yes", "True"],
["Mid", "Python", "no", "yes", "True"]]
y_test = ["True", "True", "True"]
mp_table = MyPyTable(interview_header, interview_table)
# Formulate X_train and y_train
y_train = mp_table.get_column('interviewed_well')
X_train_col_names = ["level", "lang", "tweets", "phd"]
X_train = mp_table.get_rows(X_train_col_names)
myRF = MyRandomForestClassifier(N=4, M=2, F=4)
myRF.fit(X_train, y_train)
predictions = myRF.predict(X_test)
for i in range(0, len(predictions)):
assert predictions[i] == y_test[i]
def test_random_forest_predict():
X = [["Senior", "Java", "no", "no"], ["Senior", "Java", "no", "yes"],
["Mid", "Python", "no", "no"], ["Junior", "Python", "no", "no"],
["Junior", "R", "yes", "no"], ["Junior", "R", "yes", "yes"],
["Mid", "R", "yes", "yes"], ["Senior", "Python", "no", "no"],
["Senior", "R", "yes", "no"], ["Junior", "Python", "yes", "no"],
["Senior", "Python", "yes", "yes"], ["Mid", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"], ["Junior", "Python", "no", "yes"]]
y = [
"False", "False", "True", "True", "True", "False", "True", "False",
"True", "True", "True", "True", "True", "False"
]
forest = MyRandomForestClassifier(n=4, m=2, f=2, seed=2)
forest.fit(X, y)
y_predicted = forest.predict([["Junior", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"]])
y_actual = ['False', 'True']
assert y_predicted == y_actual
def test_simple_linear_regressor_fit():
myline = MyRandomForestClassifier(2, 5, 3)
X_train = [["Senior", "Java", "no", "no"], ["Senior", "Java", "no", "yes"],
["Mid", "Python", "no", "no"], ["Junior", "Python", "no", "no"],
["Junior", "R", "yes", "no"], ["Junior", "R", "yes", "yes"],
["Mid", "R", "yes", "yes"], ["Senior", "Python", "no", "no"],
["Senior", "R", "yes", "no"], ["Junior", "Python", "yes", "no"],
["Senior", "Python", "yes", "yes"],
["Mid", "Python", "no", "yes"], ["Mid", "Java", "yes", "no"],
["Junior", "Python", "no", "yes"]]
y_train = [
"False", "False", "True", "True", "True", "False", "True", "False",
"True", "True", "True", "True", "True", "False"
]
y_domain = myutils.get_unique(y_train)
myline.fit(X_train, y_train)
prediction = myline.predict([["Junior", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"]])
for val in prediction:
assert (val in y_domain)
def test_random_forest_fit():
# interview dataset
table = [["Senior", "Java", "no", "no", "False"],
["Senior", "Java", "no", "yes", "False"],
["Mid", "Python", "no", "no", "True"],
["Junior", "Python", "no", "no", "True"],
["Junior", "R", "yes", "no", "True"],
["Junior", "R", "yes", "yes", "False"],
["Mid", "R", "yes", "yes", "True"],
["Senior", "Python", "no", "no", "False"],
["Senior", "R", "yes", "no", "True"],
["Junior", "Python", "yes", "no", "True"],
["Senior", "Python", "yes", "yes", "True"],
["Mid", "Python", "no", "yes", "True"],
["Mid", "Java", "yes", "no", "True"],
["Junior", "Python", "no", "yes", "False"]]
X, y = myutils.split_x_y_train(table)
x_train, x_test, y_train, y_test = myevaluation.train_test_split(
X, y, math.floor(len(table) * 0.33), shuffle=True)
remainder = []
for i in range(len(x_train)):
row = x_train[i]
row.append(y_train[i])
remainder.append(row)
print(remainder)
myRF = MyRandomForestClassifier()
myRF.fit(remainder, 10, 100)
y_predicted = myRF.predict(x_test)
assert len(y_predicted) == len(y_test)
count = 0
for i in range(len(y_predicted)):
if y_predicted[i] == y_test[i]:
count += 1
assert count != 0
def test_random_forest_fit():
# test on the interview dataset
# test 1
N = 2
M = 1
F = 1
trees = MyRandomForestClassifier(N = N, M = M, F = F, seed = 0)
trees.fit(X_train, y_train)
assert len(trees.learners) == M
best_tree = [x.tree for x in trees.learners][0]
check_tree_equivalence(test_tree, best_tree)
# test 2
N = 3
M = 2
F = 2
trees = MyRandomForestClassifier(N = N, M = M, F = F, seed = 0)
trees.fit(X_train, y_train)
# print([x.tree for x in trees.learners])
assert len(trees.learners) == M
tree_results = [x.tree for x in trees.learners]
for i in range(len(tree_results)):
check_tree_equivalence(tree_results[i], test_forest[i])
def test_MyRandomForestClassifier_fit():
random.seed(1)
# Interview DataSet
# Create X_train and y_train
X_train = []
y_train = []
# Append the header
X_train.append(["level", "lang", "tweets", "phd", "interviewed_well"])
# Delete the classifier
del X_train[0][-1]
# Get X_train
for row in range(len(interview_table)):
tmp = []
for col in range(len(interview_table[0]) - 1):
tmp.append(interview_table[row][col])
X_train.append(tmp)
# Get y_train
for row in range(len(interview_table)):
y_train.append(interview_table[row][-1])
# Create a MyDecisionTreeClassifier object
#print(X_train)
test_fit = MyRandomForestClassifier(100, 2, 2)
# Call fit
test_fit.fit(X_train, y_train)
# Test
#print("working")
#print(test_fit.forest)
assert (test_fit.forest[0]['atts']) == ['att0', 'att1']
assert (test_fit.forest[0]['tree'].tree) == tree_actual_1
assert (test_fit.forest[1]['atts']) == ['att0', 'att1']
assert (test_fit.forest[1]['tree'].tree) == tree_actual_2
import pickle
from mysklearn.myclassifiers import MyRandomForestClassifier
from mysklearn.mypytable import MyPyTable
import os
fname = os.path.join("input_data", "tracks_data_backup.txt")
tracks = MyPyTable().load_from_file(fname)
Danceability = tracks.get_column('danceability')
Energy = tracks.get_column('energy')
Acousticness = tracks.get_column('acousticness')
Valence = tracks.get_column('valence')
y_train = Acousticness
x_train = [[Danceability[i], Energy[i], Valence[i]]
for i in range(len(y_train))]
rf = MyRandomForestClassifier()
rf.fit(x_train, y_train, 20, 7, 2)
rf = MyRandomForestClassifier()
rf.fit(x_train, y_train, 30, 4, 2)
# serialize to file (pickle)
outfile = open("trees.p", "wb")
pickle.dump(rf.trees, outfile)
outfile.close()
# deserialize to object (unpickle)
infile = open("trees.p", "rb")
trees2 = pickle.load(infile)
infile.close()
def test_random_forest_classifier_predict():
X_train = [
["Senior", "Java", "no", "no"],
["Senior", "Java", "no", "yes"],
["Mid", "Python", "no", "no"],
["Junior", "Python", "no", "no"],
["Junior", "R", "yes", "no"],
["Junior", "R", "yes", "yes"],
["Mid", "R", "yes", "yes"],
["Senior", "Python", "no", "no"],
["Senior", "R", "yes", "no"],
["Junior", "Python", "yes", "no"],
["Senior", "Python", "yes", "yes"],
["Mid", "Python", "no", "yes"],
["Mid", "Java", "yes", "no"],
["Junior", "Python", "no", "yes"]
]
y_train = ["False", "False", "True", "True", "True", "False", "True", "False", "True", "True", "True", "True", "True", "False"]
rf = MyRandomForestClassifier()
rf.fit(X_train, y_train, 20, 7, 2)
X_test = [["Senior", "Java", "no", "no"], ["Senior", "Java", "no", "yes"], ["Mid", "Python", "no", "no"]]
pred = rf.predict(X_test)
assert pred == ["False", "False", "True"] # TODO: fix this
degrees_header = ["SoftEng", "ARIN", "HCI", "CSA", "Project", "Class"]
degrees_table = [
["A", "B", "A", "B", "B", "SECOND"],
["A", "B", "B", "B", "A", "FIRST"],
["A", "A", "A", "B", "B", "SECOND"],
["B", "A", "A", "B", "B", "SECOND"],
["A", "A", "B", "B", "A", "FIRST"],
["B", "A", "A", "B", "B", "SECOND"],
["A", "B", "B", "B", "B", "SECOND"],
["A", "B", "B", "B", "B", "SECOND"],
["A", "A", "A", "A", "A", "FIRST"],
["B", "A", "A", "B", "B", "SECOND"],
["B", "A", "A", "B", "B", "SECOND"],
["A", "B", "B", "A", "B", "SECOND"],
["B", "B", "B", "B", "A", "SECOND"],
["A", "A", "B", "A", "B", "FIRST"],
["B", "B", "B", "B", "A", "SECOND"],
["A", "A", "B", "B", "B", "SECOND"],
["B", "B", "B", "B", "B", "SECOND"],
["A", "A", "B", "A", "A", "FIRST"],
["B", "B", "B", "A", "A", "SECOND"],
["B", "B", "A", "A", "B", "SECOND"],
["B", "B", "B", "B", "A", "SECOND"],
["B", "A", "B", "A", "B", "SECOND"],
["A", "B", "B", "B", "A", "FIRST"],
["A", "B", "A", "B", "B", "SECOND"],
["B", "A", "B", "B", "B", "SECOND"],
["A", "B", "B", "B", "B", "SECOND"],
]
X_train = []
y_train = []
for row in degrees_table:
X_train.append(row[0:4])
y_train.append(row[4])
rf1 = MyRandomForestClassifier()
rf1.fit(X_train, y_train, 20, 7, 2)
test_vals = [["B", "B", "B", "B", "B"], ["A", "A", "A", "A", "A"], ["A", "A", "A", "A", "B"]]
assert rf1.predict(test_vals) == ['A', 'A', 'A']