def preprocess_and_save_1st_data(dataset_folder_path, output_path, aug=True):
"""
Preprocess Training and Validation Data
"""
if not os.path.exists(output_path):
os.makedirs(output_path)
u = util()
# Load training data ======================================================
true_data, true_label = u.load_1st_data(
os.path.join(dataset_folder_path, "crop_true"), ["0", "1", "2"])
false_data, false_label = u.load_1st_data(
os.path.join(dataset_folder_path, "crop_false"), ["3"])
data = np.append(true_data, false_data, axis=0)
label = np.append(true_label, false_label, axis=0)
pickle.dump((data, label),
open(os.path.join(output_path, 'raw_data_label.p'), 'wb'),
protocol=4)
print("[Before] data: ", np.shape(data))
# Preprocess training & validation data
train_data = np.empty((0, 360, 201, 1), float)
train_label = np.empty((0, 1), int)
test_data = np.empty((0, 360, 201, 1), float)
test_label = np.empty((0, 1), int)
idx = list(range(data.shape[0]))
random.shuffle(idx)
train_sample_num = int(0.7 * len(idx))
train_data = np.append(train_data, data[idx[1:train_sample_num]], axis=0)
train_label = np.append(train_label,
label[idx[1:train_sample_num]],
axis=0)
test_data = np.append(test_data, data[idx[train_sample_num:]], axis=0)
test_label = np.append(test_label, label[idx[train_sample_num:]], axis=0)
if aug == True:
train_data, train_label = u._augmentation(train_data, train_label)
train_data, _, _ = preprc.normalize(train_data, mean=[], std=[])
test_data, _, _ = preprc.normalize(test_data, mean=[], std=[])
# One-hot encode
train_label = preprc.one_hot_encode(train_label, dim=2)
test_label = preprc.one_hot_encode(test_label, dim=2)
print("[After] train_data shape: ", np.shape(train_data))
print("[After] train_label shape: ", np.shape(train_label))
print("[After] test_data shape: ", np.shape(test_data))
print("[After] test_label shape: ", np.shape(test_label))
# Save training data
pickle.dump((train_data, train_label),
open(os.path.join(output_path, 'preprocess_train.p'), 'wb'),
protocol=4)
pickle.dump((test_data, test_label),
open(os.path.join(output_path, 'preprocess_test.p'), 'wb'),
protocol=4)
def preprocess_and_save_data(dataset_folder_path,
output_path,
label_type,
aug=True):
"""
Preprocess Training and Validation Data
"""
if not os.path.exists(output_path):
os.makedirs(output_path)
u = util()
# Load training data ======================================================
data, label = u.load_data(os.path.join(dataset_folder_path, "crop_true"),
label_type)
pickle.dump((data, label),
open(os.path.join(output_path, 'raw_data_label.p'), 'wb'),
protocol=4)
# Preprocess training & validation data
train_data = np.empty((0, 360, 201, 1), float)
train_label = np.empty((0, 1), int)
test_data = np.empty((0, 360, 201, 1), float)
test_label = np.empty((0, 1), int)
for curr_label_type in label_type:
print("[Before] {} data: ".format(curr_label_type),
np.shape(data[curr_label_type]))
idx = list(range(data[curr_label_type].shape[0]))
random.shuffle(idx)
train_sample_num = int(0.7 * len(idx))
if aug == True:
if curr_label_type == "0":
aug_train_data, aug_train_label = u._augmentation(
data[curr_label_type][idx[1:train_sample_num]],
label[curr_label_type][idx[1:train_sample_num]],
level=1)
if curr_label_type == "1":
aug_train_data, aug_train_label = u._augmentation(
data[curr_label_type][idx[1:train_sample_num]],
label[curr_label_type][idx[1:train_sample_num]],
level=5)
print("[After] {} data: ".format(curr_label_type),
np.shape(aug_train_data))
train_data = np.append(train_data, aug_train_data, axis=0)
train_label = np.append(train_label, aug_train_label, axis=0)
if curr_label_type == "0":
test_data = np.append(test_data,
data[curr_label_type][idx[-216:]],
axis=0)
test_label = np.append(test_label,
label[curr_label_type][idx[-216:]],
axis=0)
if curr_label_type == "1":
test_data = np.append(
test_data,
data[curr_label_type][idx[train_sample_num:]],
axis=0)
test_label = np.append(
test_label,
label[curr_label_type][idx[train_sample_num:]],
axis=0)
train_data, _, _ = preprc.normalize(train_data, mean=[], std=[])
test_data, _, _ = preprc.normalize(test_data, mean=[], std=[])
# One-hot encode
train_label = preprc.one_hot_encode(train_label, dim=2)
test_label = preprc.one_hot_encode(test_label, dim=2)
print("[After] train_data shape: ", np.shape(train_data))
print("[After] train_label shape: ", np.shape(train_label))
print("[After] test_data shape: ", np.shape(test_data))
print("[After] test_label shape: ", np.shape(test_label))
# Save training data
pickle.dump((train_data, train_label),
open(os.path.join(output_path, 'preprocess_train.p'), 'wb'),
protocol=4)
pickle.dump((test_data, test_label),
open(os.path.join(output_path, 'preprocess_test.p'), 'wb'),
protocol=4)
from preprocess.make_lower_case import * from preprocess.eliminate_stop_words import * from preprocess.replace_negation_words import * from preprocess.tokenization import * from preprocess.one_hot_encode import * from preprocess.embed_200 import * from preprocess.spellingcheck import * from preprocess.extract_redundant_words import * make_lower_case = make_lower_case(0, "make_lower_case", 1) eliminate_stop_words = eliminate_stop_words(-5, "eliminate_stop_words", 2) replace_negation_words = replace_negation_words(5, "replace_negation_words", 3) tokenization = tokenization(0, "tokenization", 4) one_hot_encode = one_hot_encode(-100, "one_hot_encode", 5) spellingcheck = spellingcheck(50, "spellingcheck", 6) embed_200 = embed_200(0, "embed_200", 7)
def do_knn(df):
###################### Data preparation ####################
# df = read_mushroom_data()
df2, df2_columns = preprocess.one_hot_encode(df)
df2 = pd.DataFrame(df2, columns=df2_columns)
# print("number of samples: ", df.shape[0])
# print("number of attributes: ", df.shape[1])
# print(
# "\nValues classified as 'Missing' for stalk-root: ",
# (df.iloc[:, 11] == "?").sum(),
# )
# print("\nNumber of samples: ", df2.shape[0])
# print("Number of attributes: ", df2.shape[1])
# print(
# "\nRemaining missing values across all attributes and samples: ",
# df2.isnull().sum().sum(),
# )
# print("\nMinimum value across all attributes and samples: ", df2.min().min())
# print("Maximum value across all attributes and samples: ", df2.max().max())
# print(
# "\nMinimum fraction of '1'-s across all attributes: {:.5f}".format(
# df2.mean().min()
# )
# )
# print(
# "Maximum fraction of '1'-s across all attributes: {:.5f}".format(
# df2.mean().max()
# )
# )
kf = KFold(n_splits=5, shuffle=True)
knn = KNeighborsClassifier(n_neighbors=100)
accuracies = []
best_predictions = pd.DataFrame([], columns=df2_columns)
best_test_labels = pd.DataFrame([], columns=df2_columns)
class_columns, feature_columns = dataset_utility.get_split_column_names(
df2, get_class_column_names())
for i in range(5):
result = next(kf.split(df2), None)
# Define poisonous as 1 and edible as 0 for the target
x = df2.iloc[:, 2:]
y = df2.iloc[:, 1]
x_train = x.iloc[result[0]]
x_test = x.iloc[result[1]]
y_train = y.iloc[result[0]]
y_test = y.iloc[result[1]]
pca = PCA(n_components=2).fit(x_train)
# Reduce dimensionality of the features from 113 to two principal componets
# A PCA plot converts the correlations (or lack there of) among all of the features into a 2-D vector
x_train = pca.transform(x_train)
x_test = pca.transform(x_test)
plt.figure(dpi=120)
plt.scatter(
x_train[y_train.values == 0, 0],
x_train[y_train.values == 0, 1],
label="Edible",
alpha=0.5,
s=2,
)
plt.scatter(
x_train[y_train.values == 1, 0],
x_train[y_train.values == 1, 1],
label="Poisonous",
alpha=0.5,
s=2,
)
plt.title("Mushroom Data Set\nFirst Two Principal Components")
plt.legend(frameon=1)
plt.xlabel("PC 1")
plt.ylabel("PC 2")
plt.gca().set_aspect("equal")
plt.savefig("knn.png")
knn.fit(x_train, y_train)
y_pred = knn.predict(x_test)
# print("y_pred: ", y_pred)
acc = accuracy_score(y_pred, y_test)
# print("Accuracy:", acc)
accuracies.append(acc)
if acc >= max(accuracies):
best_predictions = y_pred
best_test_labels = y_test
print("K-fold results: ", accuracies)
print("Mean accuracy: ", np.mean(accuracies))
# print("Best prediction: ", best_prediction)
# print(metrics.confusion_matrix(best_predictions, best_test_labels))
cm_labels = ["edible", "poisonous"]
df_cm = pd.DataFrame(
metrics.confusion_matrix(best_predictions,
best_test_labels,
labels=[0.0, 1.0]),
index=cm_labels,
columns=cm_labels,
)
plt.figure(figsize=(10, 7))
sn.heatmap(df_cm, annot=True, fmt="g")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.savefig("knn-cm.png")
return class_columns
def insert_class_columns(dataset):
dt = dataset.copy()
for column in get_class_column_names():
dt.insert(df_enc.columns.get_loc(column), column, float("NaN"))
return dt
df = pd.read_csv("mushrooms.csv", names=dataset_utility.get_column_names())
df_enc, df_enc_columns = preprocess.one_hot_encode(df)
df_enc = pd.DataFrame(df_enc, columns=df_enc_columns)
train, missing = preprocess.extract_missing(df_enc)
train = pd.DataFrame(train, columns=df_enc_columns)
missing = pd.DataFrame(missing, columns=df_enc_columns)
train = train.reset_index(drop=True)
missing = missing.reset_index(drop=True)
class_columns, feature_columns = dataset_utility.get_split_column_names(
train, get_class_column_names())
missing = preprocess.remove_class_columns(missing, class_columns)
def pls_inspection(X: np.ndarray, Y: np.ndarray, n_comps: int):
n_classes = len(np.unique(Y))
Y_encoded = one_hot_encode(Y)
model = cross_decomposition.PLSRegression(n_components=n_comps,
scale=False)
model.fit(X, Y_encoded)
# Extract information
scores = model.x_scores_
loadings = model.x_loadings_
var_scores = np.var(scores, axis=0)
var_X = np.sum(np.var(X, axis=0))
var_ratios = var_scores / var_X
cum_var_ratios = np.cumsum(var_ratios)
# Colormap
cmap = plt.cm.jet
cmaplist = [cmap(i) for i in range(cmap.N)]
cmap = mpl.colors.LinearSegmentedColormap.from_list(
'Custom map', cmaplist, cmap.N)
bounds = np.linspace(0, n_classes, n_classes + 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# Explained variance plot
plt.figure(num=3, figsize=(8, 6))
plt.plot(np.pad(cum_var_ratios, (1, 0), 'constant'))
plt.title('Explained variance')
plt.xlabel('Principal components')
plt.ylabel('Cumulative explained variance')
plt.xlim((0, n_comps))
plt.ylim((0, 1))
# Loadings plot
plt.figure(num=4, figsize=(8, 6))
plt.plot(loadings[:, 0])
plt.title('PC1 loadings')
plt.figure(num=5, figsize=(8, 6))
plt.plot(loadings[:, 1])
plt.title('PC2 loadings')
plt.figure(num=6, figsize=(8, 6))
plt.plot(loadings[:, 2])
plt.title('PC3 loadings')
# 2D scores plot
plt.figure(num=7, figsize=(8, 6))
scat = plt.scatter(scores[:, 0],
scores[:, 1],
c=Y,
s=2,
cmap=cmap,
norm=norm)
cb = plt.colorbar(scat, spacing='proportional', ticks=bounds)
cb.set_label('Classes')
plt.title('Scores plot (PLS)')
plt.xlabel('PC1 ({:.2f}% explained variance)'.format(var_ratios[0] * 100))
plt.ylabel('PC2 ({:.2f}% explained variance)'.format(var_ratios[1] * 100))
# 3D scores plot
fig = plt.figure(num=8, figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
scat = ax.scatter(scores[:, 0],
scores[:, 1],
scores[:, 2],
c=Y,
s=2,
cmap=cmap,
norm=norm)
cb = plt.colorbar(scat, spacing='proportional', ticks=bounds)
cb.set_label('Classes')
ax.set_title('Scores plot (PLS)')
ax.set_xlabel('PC1 ({:.2f}% explained variance)'.format(var_ratios[0] *
100))
ax.set_ylabel('PC2 ({:.2f}% explained variance)'.format(var_ratios[1] *
100))
ax.set_zlabel('PC3 ({:.2f}% explained variance)'.format(var_ratios[2] *
100))
plt.show()
def preprocess_and_save_data(SVHN_dataset_folder_path,
output_path,
rm_class,
aug_enable=False,
reshape_enable=False):
"""
Preprocess Training and Validation Data
"""
if not os.path.exists(output_path):
os.makedirs(output_path)
# Load training data ======================================================
train_data, train_label = load_SVHN_mat(SVHN_dataset_folder_path,
"train_32x32.mat")
# Preprocess training & validation data
if aug_enable == True:
train_data_ud = preprc.vertical_flip(train_data)
train_data_lr = preprc.horizontal_flip(train_data)
train_data = np.concatenate((train_data, train_data_ud, train_data_lr))
train_label = np.concatenate((train_label, train_label, train_label))
if reshape_enable == True:
train_data = preprc.reshape_image(train_data, (64, 64, 3))
print("[Training data] Removing No.{} Class...".format(rm_class))
print("\t[Before] train_data shape: ", np.shape(train_data))
print("\t[Before] train_label shape: ", np.shape(train_label))
idx = np.squeeze(train_label != rm_class)
train_data = train_data[idx]
train_label = train_label[idx]
train_data, _, _ = preprc.normalize(train_data, mean=[], std=[])
train_label = preprc.one_hot_encode(train_label)
print("\t[After] train_data shape: ", np.shape(train_data))
print("\t[After] train_label shape: ", np.shape(train_label))
# Save training data
pickle.dump((train_data, train_label),
open(
os.path.join(output_path,
'preprocess_train_{}.p'.format(rm_class)),
'wb'),
protocol=4)
# Load Testing data =======================================================
test_data, test_label = load_SVHN_mat(SVHN_dataset_folder_path,
"test_32x32.mat")
if reshape_enable == True:
test_data = preprc.reshape_image(test_data, (64, 64, 3))
print("[Testing data] Removing No.{} Class...".format(rm_class))
print("\t[Before] test_data shape: ", np.shape(test_data))
print("\t[Before] test_label shape: ", np.shape(test_label))
idx = np.squeeze(test_label != rm_class)
test_data_rm = test_data[idx]
test_label_rm = test_label[idx]
print("\t[After] test_data shape: ", np.shape(test_data_rm))
print("\t[After] test_label shape: ", np.shape(test_label_rm))
# Preprocess training & validation data
test_data, _, _ = preprc.normalize(test_data, mean=[], std=[])
test_label = preprc.one_hot_encode(test_label)
test_data_rm, _, _ = preprc.normalize(test_data_rm, mean=[], std=[])
test_label_rm = preprc.one_hot_encode(test_label_rm)
# Save original test data
pickle.dump((np.array(test_data), np.array(test_label)),
open(os.path.join(output_path, 'test.p'), 'wb'))
# Save test data
pickle.dump((np.array(test_data_rm), np.array(test_label_rm)),
open(
os.path.join(output_path,
'preprocess_test_{}.p'.format(rm_class)),
'wb'),
protocol=4)
def preprocess_and_save_single_class_data(SVHN_dataset_folder_path,
output_path,
aug_enable=False,
reshape_enable=False):
"""
Preprocess Training and Validation Data
"""
if not os.path.exists(output_path):
os.makedirs(output_path)
# Load training data ======================================================
train_data, train_label = load_SVHN_mat(SVHN_dataset_folder_path,
"train_32x32.mat")
# Preprocess training & validation data
if aug_enable == True:
train_data_ud = preprc.vertical_flip(train_data)
train_data_lr = preprc.horizontal_flip(train_data)
train_data = np.concatenate((train_data, train_data_ud, train_data_lr))
train_label = np.concatenate((train_label, train_label, train_label))
if reshape_enable == True:
train_data = preprc.reshape_image(train_data, (64, 64, 3))
train_data, _, _ = preprc.normalize(train_data, mean=[], std=[])
train_label = preprc.one_hot_encode(train_label)
for reserved_class in range(10):
print(
"[Training data] Extracting No.{} Class...".format(reserved_class))
curr_features = train_data[train_label[:, reserved_class] == 1]
curr_lables = train_label[train_label[:, reserved_class] == 1]
print("\t[Class {}] feature shape: ".format(reserved_class),
np.shape(curr_features))
print(np.min(curr_features))
print(np.max(curr_features))
# Save training data
pickle.dump(
(curr_features, curr_lables),
open(
os.path.join(output_path,
'pr_train_class_{}.p'.format(reserved_class)),
'wb'))
# Load Testing data =======================================================
test_data, test_label = load_SVHN_mat(SVHN_dataset_folder_path,
"test_32x32.mat")
if reshape_enable == True:
test_data = preprc.reshape_image(test_data, (64, 64, 3))
# Preprocess training & validation data
test_data, _, _ = preprc.normalize(test_data, mean=[], std=[])
test_label = preprc.one_hot_encode(test_label)
# Save original test data
pickle.dump((np.array(test_data), np.array(test_label)),
open(os.path.join(output_path, 'test.p'), 'wb'))
for reserved_class in range(10):
print(
"[Testing data] Extracting No.{} Class...".format(reserved_class))
curr_features = test_data[test_label[:, reserved_class] == 1]
curr_lables = test_label[test_label[:, reserved_class] == 1]
print("\t[After] feature shape: ", np.shape(curr_features))
print(np.min(curr_features))
print(np.max(curr_features))
# Save test data
pickle.dump(
(np.array(curr_features), np.array(curr_lables)),
open(
os.path.join(output_path,
'pr_test_class_{}.p'.format(reserved_class)),
'wb'))
def preprocess_and_save_data(cifar10_dataset_folder_path, output_path,
rm_class, aug_enable, reshape_enable):
"""
Preprocess Training and Validation Data
"""
n_batches = 5
if not os.path.exists(output_path):
os.makedirs(output_path)
features = []
labels = []
for batch_i in range(1, n_batches + 1):
curr_features, curr_labels = load_cfar10_batch(
cifar10_dataset_folder_path, batch_i)
if len(features) is 0:
features = curr_features
labels = curr_labels
else:
features = np.concatenate((features, curr_features))
labels = np.concatenate((labels, curr_labels))
# Preprocess training & validation data
if aug_enable == True:
features_ud = preprc.vertical_flip(features)
features_lr = preprc.horizontal_flip(features)
features_rot90 = preprc.rot90(features)
features_rot270 = preprc.rot270(features)
features = np.concatenate((features, features_ud, features_lr,
features_rot90, features_rot270))
labels = np.concatenate((labels, labels, labels, labels, labels))
if reshape_enable == True:
features = preprc.reshape_image(features, (64, 64, 3))
features, _, _ = preprc.normalize(features, mean=mean, std=std)
labels = preprc.one_hot_encode(labels)
print("[Training data] Removing No.{} Class...".format(rm_class))
print("\t[Before] feature shape: ", np.shape(features))
print("\t[Before] label shape: ", np.shape(labels))
count = 0
remove_class = []
for i in range(len(features)):
if labels[i, rm_class] == 1:
count = count + 1
remove_class.append(i)
print("\tCount: {}".format(count))
features = np.delete(features, remove_class, axis=0)
labels = np.delete(labels, remove_class, axis=0)
print("\t[After] feature shape: ", np.shape(features))
print("\t[After] label shape: ", np.shape(labels))
# Save training data
pickle.dump((features, labels),
open(
os.path.join(output_path,
'preprocess_train_{}.p'.format(rm_class)),
'wb'),
protocol=4)
with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:
batch = pickle.load(file, encoding='latin1')
# load the test data
test_features = batch['data'].reshape(
(len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
test_labels = batch['labels']
if reshape_enable == True:
test_features = preprc.reshape_image(test_features, (64, 64, 3))
# Preprocess training & validation data
test_features, _, _ = preprc.normalize(test_features, mean=mean, std=std)
test_labels = preprc.one_hot_encode(test_labels)
# Save original test data
pickle.dump((np.array(test_features), np.array(test_labels)),
open(os.path.join(output_path, 'test.p'), 'wb'))
print("[Testing data] Removing No.{} Class...".format(rm_class))
print("\t[Before] feature shape: ", np.shape(test_features))
print("\t[Before] label shape: ", np.shape(test_labels))
count = 0
remove_class = []
for i in range(len(test_features)):
if test_labels[i, rm_class] == 1:
count = count + 1
remove_class.append(i)
print("\tCount: {}".format(count))
test_features = np.delete(test_features, remove_class, axis=0)
test_labels = np.delete(test_labels, remove_class, axis=0)
print("\t[After] feature shape: ", np.shape(test_features))
print("\t[After] label shape: ", np.shape(test_labels))
# Save test data
pickle.dump((np.array(test_features), np.array(test_labels)),
open(
os.path.join(output_path,
'preprocess_test_{}.p'.format(rm_class)),
'wb'),
protocol=4)
def preprocess_and_save_single_class_data(cifar10_dataset_folder_path,
output_path, aug_enable,
reshape_enable):
"""
Preprocess Training and Validation Data
"""
n_batches = 5
if not os.path.exists(output_path):
os.makedirs(output_path)
features = []
labels = []
for batch_i in range(1, n_batches + 1):
curr_features, curr_labels = load_cfar10_batch(
cifar10_dataset_folder_path, batch_i)
if len(features) is 0:
features = curr_features
labels = curr_labels
else:
features = np.concatenate((features, curr_features))
labels = np.concatenate((labels, curr_labels))
# Preprocess training & validation data
if aug_enable == True:
features_ud = preprc.vertical_flip(features)
features_lr = preprc.horizontal_flip(features)
features_rot90 = preprc.rot90(features)
features_rot270 = preprc.rot270(features)
features = np.concatenate((features, features_ud, features_lr,
features_rot90, features_rot270))
labels = np.concatenate((labels, labels, labels, labels, labels))
if reshape_enable == True:
features = preprc.reshape_image(features, (64, 64, 3))
features, _, _ = preprc.normalize(features, mean=mean, std=std)
labels = preprc.one_hot_encode(labels)
for reserved_class in range(10):
print(
"[Training data] Extracting No.{} Class...".format(reserved_class))
curr_features = features[labels[:, reserved_class] == 1]
curr_lables = labels[labels[:, reserved_class] == 1]
print("\t[Class {}] feature shape: ".format(reserved_class),
np.shape(curr_features))
# Save training data
pickle.dump(
(curr_features, curr_lables),
open(
os.path.join(output_path,
'pr_train_class_{}.p'.format(reserved_class)),
'wb'))
with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:
batch = pickle.load(file, encoding='latin1')
# load the test data
test_features = batch['data'].reshape(
(len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)
test_labels = batch['labels']
if reshape_enable == True:
test_features = preprc.reshape_image(test_features, (64, 64, 3))
# Preprocess training & validation data
test_features, _, _ = preprc.normalize(test_features, mean=mean, std=std)
test_labels = preprc.one_hot_encode(test_labels)
# Save original test data
pickle.dump((np.array(test_features), np.array(test_labels)),
open(os.path.join(output_path, 'test.p'), 'wb'))
for reserved_class in range(10):
print(
"[Testing data] Extracting No.{} Class...".format(reserved_class))
curr_features = test_features[test_labels[:, reserved_class] == 1]
curr_lables = test_labels[test_labels[:, reserved_class] == 1]
print("\t[After] feature shape: ", np.shape(curr_features))
# Save test data
pickle.dump(
(np.array(curr_features), np.array(curr_lables)),
open(
os.path.join(output_path,
'pr_test_class_{}.p'.format(reserved_class)),
'wb'))
def do_decision_tree(df):
df_enc, df_enc_columns = preprocess.one_hot_encode(df)
df_enc = pd.DataFrame(df_enc, columns=df_enc_columns)
# ---KFold cross validation---
k_fold_splits = 5
kf = KFold(n_splits=k_fold_splits, shuffle=True)
accuracies = []
best_predictions = pd.DataFrame([], columns=get_class_column_names())
best_test_labels = pd.DataFrame([], columns=get_class_column_names())
dt = DecisionTreeClassifier(random_state=0,
max_depth=4,
min_samples_leaf=5)
class_columns, feature_columns = dataset_utility.get_split_column_names(
df_enc, get_class_column_names())
features, labels = preprocess.split_features_labels(df_enc, class_columns)
for i in range(k_fold_splits):
result = next(kf.split(df_enc), None)
train_features = features.iloc[result[0]]
test_features = features.iloc[result[1]]
train_labels = labels.iloc[result[0]]
test_labels = labels.iloc[result[1]]
# ---Decision Tree----
dt.fit(train_features, train_labels)
predictions = dt.predict(test_features)
accuracy = metrics.accuracy_score(predictions, test_labels)
accuracies.append(accuracy)
if accuracy >= max(accuracies):
best_predictions = pd.DataFrame(predictions,
columns=get_class_column_names())
best_test_labels = pd.DataFrame(test_labels,
columns=get_class_column_names())
# best_predictions = pd.DataFrame(predictions, columns=get_class_column_names())
best_predictions = best_predictions.idxmax(axis=1)
best_test_labels = best_test_labels.idxmax(axis=1)
df_cm = pd.DataFrame(
metrics.confusion_matrix(best_predictions,
best_test_labels,
labels=get_class_column_names()),
index=cm_labels,
columns=cm_labels,
)
plt.figure(figsize=(10, 7))
sn.heatmap(df_cm, annot=True, fmt="g")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.savefig("decision-tree-cm.png")
print("K-fold results: ", accuracies)
print("Mean accuracy: ", np.mean(accuracies))
fig = plt.figure(figsize=(25, 20))
_ = tree.plot_tree(
dt,
feature_names=feature_columns,
class_names=class_columns,
filled=True,
rounded=True,
)
fig.savefig("decision_tree.png")
