def backward_selection(self, max_features, X_train, y_train):
# First select all features.
selected_features = X_train.columns.tolist()
ca = ClassificationAlgorithms()
ce = ClassificationEvaluation()
for i in range(0, (len(X_train.columns) - max_features)):
best_perf = 0
worst_feature = ''
# Select from the features that are still in the selection.
for f in selected_features:
temp_selected_features = copy.deepcopy(selected_features)
temp_selected_features.remove(f)
# Determine the score without the feature.
pred_y_train, pred_y_test, prob_training_y, prob_test_y = ca.decision_tree(X_train[temp_selected_features], y_train, X_train[temp_selected_features])
perf = ce.accuracy(y_train, pred_y_train)
# If we score better without the feature than what we have seen so far
# this is the worst feature.
if perf > best_perf:
best_perf = perf
worst_feature = f
# Remove the worst feature.
selected_features.remove(worst_feature)
return selected_features
def forward_selection(
max_features: int, X_train: pd.DataFrame,
y_train: pd.Series) -> Tuple[List[str], List[str], List[float]]:
"""
Select the given number of features for classification, that show the best accuracy, using forward selection.
The method uses the given features and labels to train a decision tree and determine the accuracy of the
prediction. The method returns the selected features as well as the the scores.
:param max_features: Number of features to select.
:param X_train: Features as DataFrame.
:param y_train: Labels corresponding to given features.
:return: Selected features and scores.
"""
# Start with no features
ordered_features = []
ordered_scores = []
selected_features = []
ca = ClassificationAlgorithms()
ce = ClassificationEvaluation()
# Select the appropriate number of features
for i in range(0, max_features):
# Determine the features left to select
features_left = list(set(X_train.columns) - set(selected_features))
best_perf = 0
best_feature = ''
print(f'Selecting feature {i+1}/{max_features}')
# Iterate over all features left
for f in features_left:
temp_selected_features = copy.deepcopy(selected_features)
temp_selected_features.append(f)
# Determine the accuracy of a decision tree learner adding the feature
pred_y_train, pred_y_test, prob_training_y, prob_test_y = ca.decision_tree(
X_train[temp_selected_features],
y_train,
X_train[temp_selected_features],
gridsearch=False)
perf = ce.accuracy(y_train, pred_y_train)
# If the performance is better than the best so far (aiming for high accuracy), set the current feature
# to the best feature and the same for the best performance
if perf > best_perf:
best_perf = perf
best_feature = f
# Select the feature with the best performance
selected_features.append(best_feature)
ordered_features.append(best_feature)
ordered_scores.append(best_perf)
return selected_features, ordered_features, ordered_scores
def forward_selection(self, max_features, X_train, y_train):
# Start with no features.
ordered_features = []
ordered_scores = []
selected_features = []
ca = ClassificationAlgorithms()
ce = ClassificationEvaluation()
prev_best_perf = 0
# Select the appropriate number of features.
for i in range(0, max_features):
print i
#Determine the features left to select.
features_left = list(set(X_train.columns) - set(selected_features))
best_perf = 0
best_attribute = ''
# For all features we can still select...
for f in features_left:
temp_selected_features = copy.deepcopy(selected_features)
temp_selected_features.append(f)
# Determine the accuracy of a decision tree learner if we were to add
# the feature.
pred_y_train, pred_y_test, prob_training_y, prob_test_y = ca.decision_tree(
X_train[temp_selected_features], y_train,
X_train[temp_selected_features])
perf = ce.accuracy(y_train, pred_y_train)
# If the performance is better than what we have seen so far (we aim for high accuracy)
# we set the current feature to the best feature and the same for the best performance.
if perf > best_perf:
best_perf = perf
best_feature = f
# We select the feature with the best performance.
selected_features.append(best_feature)
prev_best_perf = best_perf
ordered_features.append(best_feature)
ordered_scores.append(best_perf)
return selected_features, ordered_features, ordered_scores
def backward_selection(max_features: int, X_train: pd.DataFrame,
y_train: pd.Series) -> List[str]:
"""
Select the given number of features for classification, that show the best accuracy, using backward selection.
The method uses the given features and labels to train a decision tree and determine the accuracy of the
prediction.
:param max_features: Number of features to select.
:param X_train: Features as DataFrame.
:param y_train: Labels corresponding to given features.
:return: Selected features.
"""
# First select all features
selected_features = X_train.columns.tolist()
ca = ClassificationAlgorithms()
ce = ClassificationEvaluation()
for i in range(0, (len(X_train.columns) - max_features)):
best_perf = 0
worst_feature = ''
# Select from the features that are still in the selection
for f in selected_features:
temp_selected_features = copy.deepcopy(selected_features)
temp_selected_features.remove(f)
# Determine the score without the feature
pred_y_train, pred_y_test, prob_training_y, prob_test_y = ca.decision_tree(
X_train[temp_selected_features], y_train,
X_train[temp_selected_features])
perf = ce.accuracy(y_train, pred_y_train)
# If scoring better without the feature than seen so far, this is the worst feature
if perf > best_perf:
best_perf = perf
worst_feature = f
# Remove the worst feature
selected_features.remove(worst_feature)
return selected_features
def main():
# Read the result from the previous chapter and convert the index to datetime
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FILENAME, index_col=0)
dataset.index = pd.to_datetime(dataset.index)
except IOError as e:
print(
'File not found, try to run previous crowdsignals scripts first!')
raise e
# Create an instance of visualization class to plot the results
DataViz = VisualizeDataset(__file__)
# Consider the first task, namely the prediction of the label. Therefore create a single column with the categorical
# attribute representing the class. Furthermore, use 70% of the data for training and the remaining 30% as an
# independent test set. Select the sets based on stratified sampling and remove cases where the label is unknown.
print('\n- - - Loading dataset - - -')
prepare = PrepareDatasetForLearning()
learner = ClassificationAlgorithms()
evaluation = ClassificationEvaluation()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_classification(
dataset, ['label'], 'like', 0.7, filter_data=True, temporal=False)
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features
print('- - - Selecting subsets - - -')
basic_features = [
'acc_phone_x', 'acc_phone_y', 'acc_phone_z', 'acc_watch_x',
'acc_watch_y', 'acc_watch_z', 'gyr_phone_x', 'gyr_phone_y',
'gyr_phone_z', 'gyr_watch_x', 'gyr_watch_y', 'gyr_watch_z',
'hr_watch_rate', 'light_phone_lux', 'mag_phone_x', 'mag_phone_y',
'mag_phone_z', 'mag_watch_x', 'mag_watch_y', 'mag_watch_z',
'press_phone_pressure'
]
pca_features = [
'pca_1', 'pca_2', 'pca_3', 'pca_4', 'pca_5', 'pca_6', 'pca_7'
]
time_features = [name for name in dataset.columns if '_temp_' in name]
freq_features = [
name for name in dataset.columns
if (('_freq' in name) or ('_pse' in name))
]
cluster_features = ['cluster']
print('#basic features: ', len(basic_features))
print('#PCA features: ', len(pca_features))
print('#time features: ', len(time_features))
print('#frequency features: ', len(freq_features))
print('#cluster features: ', len(cluster_features))
features_after_chapter_3 = list(set().union(basic_features, pca_features))
features_after_chapter_4 = list(set().union(features_after_chapter_3,
time_features, freq_features))
features_after_chapter_5 = list(set().union(features_after_chapter_4,
cluster_features))
if FLAGS.mode == 'selection' or FLAGS.mode == 'all':
# First, consider the performance over a selection of features
N_FORWARD_SELECTION = FLAGS.nfeatures
fs = FeatureSelectionClassification()
print('\n- - - Running feature selection - - -')
features, ordered_features, ordered_scores = fs.forward_selection(
max_features=N_FORWARD_SELECTION,
X_train=train_X[features_after_chapter_5],
y_train=train_y)
DataViz.plot_xy(x=[range(1, N_FORWARD_SELECTION + 1)],
y=[ordered_scores],
xlabel='number of features',
ylabel='accuracy')
# Select the most important features (based on python2 features)
selected_features = [
'acc_phone_y_freq_0.0_Hz_ws_40',
'press_phone_pressure_temp_mean_ws_120', 'gyr_phone_x_temp_std_ws_120',
'mag_watch_y_pse', 'mag_phone_z_max_freq', 'gyr_watch_y_freq_weighted',
'gyr_phone_y_freq_1.0_Hz_ws_40', 'acc_phone_x_freq_1.9_Hz_ws_40',
'mag_watch_z_freq_0.9_Hz_ws_40', 'acc_watch_y_freq_0.5_Hz_ws_40'
]
if FLAGS.mode == 'regularization' or FLAGS.mode == 'all':
print('\n- - - Running regularization and model complexity test - - -')
# Study the impact of regularization and model complexity: does regularization prevent overfitting?
# Due to runtime constraints run the experiment 3 times, for even more robust data increase the repetitions
N_REPEATS_NN = FLAGS.nnrepeat
reg_parameters = [0.0001, 0.001, 0.01, 0.1, 1, 10]
performance_training = []
performance_test = []
for reg_param in reg_parameters:
performance_tr = 0
performance_te = 0
for i in range(0, N_REPEATS_NN):
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(
train_X,
train_y,
test_X,
hidden_layer_sizes=(250, ),
alpha=reg_param,
max_iter=500,
gridsearch=False)
performance_tr += evaluation.accuracy(train_y, class_train_y)
performance_te += evaluation.accuracy(test_y, class_test_y)
performance_training.append(performance_tr / N_REPEATS_NN)
performance_test.append(performance_te / N_REPEATS_NN)
DataViz.plot_xy(x=[reg_parameters, reg_parameters],
y=[performance_training, performance_test],
method='semilogx',
xlabel='regularization parameter value',
ylabel='accuracy',
ylim=[0.95, 1.01],
names=['training', 'test'],
line_styles=['r-', 'b:'])
if FLAGS.mode == 'tree' or FLAGS.mode == 'all':
print('\n- - - Running leaf size test of decision tree - - -')
# Consider the influence of certain parameter settings for the tree model. (very related to the
# regularization) and study the impact on performance.
leaf_settings = [1, 2, 5, 10]
performance_training = []
performance_test = []
for no_points_leaf in leaf_settings:
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.decision_tree(
train_X[selected_features],
train_y,
test_X[selected_features],
min_samples_leaf=no_points_leaf,
gridsearch=False,
print_model_details=False)
performance_training.append(
evaluation.accuracy(train_y, class_train_y))
performance_test.append(evaluation.accuracy(test_y, class_test_y))
DataViz.plot_xy(x=[leaf_settings, leaf_settings],
y=[performance_training, performance_test],
xlabel='Minimum number of points per leaf',
ylabel='Accuracy',
names=['training', 'test'],
line_styles=['r-', 'b:'])
if FLAGS.mode == 'overall' or FLAGS.mode == 'all':
print(
'\n- - - Running test of all different classification algorithms - - -'
)
# Perform grid searches over the most important parameters and do so by means of cross validation upon the
# training set
possible_feature_sets = [
basic_features, features_after_chapter_3, features_after_chapter_4,
features_after_chapter_5, selected_features
]
feature_names = [
'initial set', 'Chapter 3', 'Chapter 4', 'Chapter 5',
'Selected features'
]
N_KCV_REPEATS = FLAGS.kcvrepeat
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
selected_train_X = train_X[possible_feature_sets[i]]
selected_test_X = test_X[possible_feature_sets[i]]
# First run non deterministic classifiers a number of times to average their score
performance_tr_nn, performance_te_nn = 0, 0
performance_tr_rf, performance_te_rf = 0, 0
performance_tr_svm, performance_te_svm = 0, 0
for repeat in range(0, N_KCV_REPEATS):
print(
f'Training NeuralNetwork run {repeat + 1} / {N_KCV_REPEATS}, featureset is {feature_names[i]} ... '
)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(
selected_train_X,
train_y,
selected_test_X,
gridsearch=True)
print(
f'Training RandomForest run {repeat + 1} / {N_KCV_REPEATS}, featureset is {feature_names[i]} ... '
)
performance_tr_nn += evaluation.accuracy(
train_y, class_train_y)
performance_te_nn += evaluation.accuracy(test_y, class_test_y)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest(
selected_train_X,
train_y,
selected_test_X,
gridsearch=True)
performance_tr_rf += evaluation.accuracy(
train_y, class_train_y)
performance_te_rf += evaluation.accuracy(test_y, class_test_y)
print(
f'Training SVM run {repeat + 1} / {N_KCV_REPEATS}, featureset is {feature_names[i]} ...'
)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner. \
support_vector_machine_with_kernel(selected_train_X, train_y, selected_test_X, gridsearch=True)
performance_tr_svm += evaluation.accuracy(
train_y, class_train_y)
performance_te_svm += evaluation.accuracy(test_y, class_test_y)
overall_performance_tr_nn = performance_tr_nn / N_KCV_REPEATS
overall_performance_te_nn = performance_te_nn / N_KCV_REPEATS
overall_performance_tr_rf = performance_tr_rf / N_KCV_REPEATS
overall_performance_te_rf = performance_te_rf / N_KCV_REPEATS
overall_performance_tr_svm = performance_tr_svm / N_KCV_REPEATS
overall_performance_te_svm = performance_te_svm / N_KCV_REPEATS
# Run deterministic classifiers:
print("Deterministic Classifiers:")
print(
f'Training Nearest Neighbor run 1 / 1, featureset {feature_names[i]}'
)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.k_nearest_neighbor(
selected_train_X, train_y, selected_test_X, gridsearch=True)
performance_tr_knn = evaluation.accuracy(train_y, class_train_y)
performance_te_knn = evaluation.accuracy(test_y, class_test_y)
print(
f'Training Decision Tree run 1 / 1 featureset {feature_names[i]}'
)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.decision_tree(
selected_train_X, train_y, selected_test_X, gridsearch=True)
performance_tr_dt = evaluation.accuracy(train_y, class_train_y)
performance_te_dt = evaluation.accuracy(test_y, class_test_y)
print(
f'Training Naive Bayes run 1/1 featureset {feature_names[i]}')
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.naive_bayes(
selected_train_X, train_y, selected_test_X)
performance_tr_nb = evaluation.accuracy(train_y, class_train_y)
performance_te_nb = evaluation.accuracy(test_y, class_test_y)
scores_with_sd = util. \
print_table_row_performances(feature_names[i], len(selected_train_X.index),
len(selected_test_X.index), [
(overall_performance_tr_nn, overall_performance_te_nn),
(overall_performance_tr_rf, overall_performance_te_rf),
(overall_performance_tr_svm, overall_performance_te_svm),
(performance_tr_knn, performance_te_knn),
(performance_tr_knn, performance_te_knn),
(performance_tr_dt, performance_te_dt),
(performance_tr_nb, performance_te_nb)])
scores_over_all_algs.append(scores_with_sd)
DataViz.plot_performances_classification(
['NN', 'RF', 'SVM', 'KNN', 'DT', 'NB'], feature_names,
scores_over_all_algs)
if FLAGS.mode == 'detail' or FLAGS.mode == 'all':
print(
'\n- - - Running detail test of promising classification algorithms - - -'
)
# Study two promising ones in more detail, namely decision tree and random forest algorithm
learner.decision_tree(train_X[selected_features],
train_y,
test_X[selected_features],
gridsearch=True,
print_model_details=True,
export_tree_path=EXPORT_TREE_PATH)
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest(
train_X[selected_features],
train_y,
test_X[selected_features],
gridsearch=True,
print_model_details=True)
test_cm = evaluation.confusion_matrix(test_y, class_test_y,
class_train_prob_y.columns)
DataViz.plot_confusion_matrix(test_cm,
class_train_prob_y.columns,
normalize=False)
'mag_y_freq_0.8_Hz_ws_40', 'pca_7_temp_std_ws_120', 'mag_x_max_freq',
'gyr_z_freq_2.0_Hz_ws_40', 'gyr_y_freq_0.0_Hz_ws_40',
'mag_z_freq_1.5_Hz_ws_40', 'acc_z_temp_MAD_ws_120',
'acc_y_temp_kurtosis_ws_120', 'mag_x_freq_1.2_Hz_ws_40',
'lin_acc_y_freq_1.8_Hz_ws_40'
]
selected_features_with_DT = [
'acc_z_freq_0.0_Hz_ws_40', 'loc_height_temp_mean_ws_120',
'pca_4_temp_kurtosis_ws_120', 'lin_acc_y_temp_kurtosis_ws_120',
'pca_1_temp_kurtosis_ws_120', 'acc_z_temp_MAD_ws_120',
'mag_x_freq_1.2_Hz_ws_40', 'gyr_z_freq_2.0_Hz_ws_40',
'acc_y_temp_kurtosis_ws_120', 'lin_acc_y_freq_0.6_Hz_ws_40'
]
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
possible_feature_sets = [
basic_features, features_after_outliers_and_imputation,
features_after_domain_features, features_after_cluster_features,
selected_features_with_DT, selected_features_with_NB
]
feature_names = [
'initial set', 'After imputation', 'With Domain features',
'With cluster features', 'Selected features DT', 'Selected features NB'
]
repeats = 5
scores_over_all_algs = []
plot.ylabel('accuracy')
plot.show()
# Based on the plot we select the top 10 features.
'''
selected_features = [
'acc_phone_y_freq_0.0_Hz_ws_40', 'press_phone_pressure_temp_mean_ws_120',
'gyr_phone_x_temp_std_ws_120', 'mag_watch_y_pse', 'mag_phone_z_max_freq',
'gyr_watch_y_freq_weighted', 'gyr_phone_y_freq_1.0_Hz_ws_40',
'acc_phone_x_freq_1.9_Hz_ws_40', 'mag_watch_z_freq_0.9_Hz_ws_40',
'acc_watch_y_freq_0.5_Hz_ws_40'
]
# Let us first study the impact of regularization and model complexity: does regularization prevent overfitting?
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
'''
reg_parameters = [0.0001, 0.001, 0.01, 0.1, 1, 10]
performance_training = []
performance_test = []
# We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
repeats = 20
for reg_param in reg_parameters:
performance_tr = 0
performance_te = 0
for i in range(0, repeats):
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(train_X, train_y,
result.columns = result.columns.astype(str)
# result = result.sample(frac=1)
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_classification(result, ['class'], 'unlike', 0.8,
filter=True, temporal=False)
#number_training_samples = len(train_X)
#val_split = int(0.7 * number_training_samples)
#val_X = train_X[val_split:-1]
#val_y = train_y[val_split:-1]
#train_X = train_X[0:val_split - 1]
#train_y = train_y[0:val_split - 1]
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
print(len(train_X))
print(len(test_X))
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(train_X,
train_y,
test_X,
hidden_layer_sizes=(
250, 50,),
# alpha=reg_param,
max_iter=500,
gridsearch=False)
performance_tr_nn = eval.accuracy(train_y, class_train_y)
# Based on the plot we select the top 10 features.
exit(0)
# selected_features = ['acc_phone_y_freq_0.0_Hz_ws_40', 'press_phone_pressure_temp_mean_ws_120', 'gyr_phone_x_temp_std_ws_120',
# 'mag_watch_y_pse', 'mag_phone_z_max_freq', 'gyr_watch_y_freq_weighted', 'gyr_phone_y_freq_1.0_Hz_ws_40',
# 'acc_phone_x_freq_1.9_Hz_ws_40', 'mag_watch_z_freq_0.9_Hz_ws_40', 'acc_watch_y_freq_0.5_Hz_ws_40']
selected_features = [
'pca_1_temp_mean_ws_120', 'press_phone_Pressure_temp_mean_ws_120',
'acc_phone_x_freq_0.0_Hz_ws_40', 'mag_phone_y_temp_mean_ws_120',
'gyr_phone_z_freq_0.7_Hz_ws_40', 'pca_3', 'mag_phone_z_freq_0.9_Hz_ws_40',
'mag_phone_z_freq_0.7_Hz_ws_40', 'gyr_phone_z_freq_0.5_Hz_ws_40',
'gyr_phone_y_freq_0.6_Hz_ws_40'
] # forward feature selection
# # Let us first study the impact of regularization and model complexity: does regularization prevent overfitting?
#
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
#
# reg_parameters = [0.0001, 0.001, 0.01, 0.1, 1, 10]
# performance_training = []
# performance_test = []
#
# # We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
#
# repeats = 2
#
# for reg_param in reg_parameters:
# performance_tr = 0
# performance_te = 0
# for i in range(0, repeats):
# class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(train_X, train_y,
freq_features = [name for name in dataset.columns if (('_freq' in name) or ('_pse' in name))]
print '#basic features: ', len(basic_features)
print '#PCA features: ', len(pca_features)
print '#time features: ', len(time_features)
print '#frequency features: ', len(freq_features)
cluster_features = ['cluster']
print '#cluster features: ', len(cluster_features)
features_after_chapter_3 = list(set().union(basic_features, pca_features))
features_after_chapter_4 = list(set().union(basic_features, pca_features, time_features, freq_features))
features_after_chapter_5 = list(set().union(basic_features, pca_features, time_features, freq_features, cluster_features))
# First, let us consider the performance over a selection of features:
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
# And we study two promising ones in more detail. First let us consider the decision tree which works best with the selected
# features.
#
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.decision_tree(train_X[features_after_chapter_5], train_y, test_X[features_after_chapter_5],
gridsearch=True,
print_model_details=True, export_tree_path=export_tree_path)
#class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest(train_X[features_after_chapter_5], train_y, test_X[features_after_chapter_5],
# gridsearch=True, print_model_details=True)
test_cm = eval.confusion_matrix(test_y, class_test_y, class_train_prob_y.columns)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plot
from Chapter7.LearningAlgorithms import ClassificationAlgorithms
data = pd.read_table("../datasets/our_data/plrx.txt")
algs = ClassificationAlgorithms()
data['label'] -= 1
train = data.head(130)
test = data.tail(52)
train_X = train[[str(i) for i in range(1, 13)]]
train_y = train['label']
test_X = test[[str(i) for i in range(1, 13)]]
test_y = test['label']
res = algs.feedforward_neural_network(train_X, train_y, test_X)
def roc_curve_creation(P):
roc_curve_points = []
P = sorted(P, key=lambda x: x[0], reverse=True)
pos = 0
for pair in P:
pos += pair[1]
neg = len(P) - pos
last = -1
for i in range(len(P)):
if P[i][0] == last:
continue
last = P[i][0]
P_sel = P[i:]
count = 0
def experiment(file):
dataset = pd.read_csv(file, index_col=time_col)
DataViz = VisualizeDataset(__file__.split('.')[0] +
file.split('.')[0].split('/')[1] + '.py',
show=True)
print(DataViz.figures_dir)
dataset.index = pd.to_datetime(dataset.index)
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_classification(
dataset, ['label'],
'like',
0.7,
filter=False,
temporal=False,
drop_na=False,
fill_na=True)
time_features = [name for name in dataset.columns if '_temp' in name]
freq_features = [
name for name in dataset.columns
if (('_freq' in name) or ('_pse' in name))
]
cluster_features = ['cluster']
features_2 = list(set().union(basic_features, time_features))
features_3 = list(set().union(basic_features, time_features,
freq_features))
features_4 = list(set().union(basic_features, time_features, freq_features,
cluster_features))
# print('feature selection')
# fs = FeatureSelectionClassification()
# features, selected_features, ordered_scores = fs.forward_selection(N_FORWARD_SELECTION,
# train_X[features_4], train_y)
# log([str(ordered_scores), str(selected_features)])
selected_features = [
'gyr_y_temp_std_ws_1200', 'acc_z_temp_mean_ws_120',
'acc_x_temp_mean_ws_120', 'gyr_x_temp_std_ws_2400', 'gyr_z_max_freq',
'gyr_y_freq_1.9_Hz_ws_40', 'acc_z_freq_0.4_Hz_ws_40',
'gyr_z_freq_1.2_Hz_ws_40', 'gyr_x_freq_0.2_Hz_ws_40',
'acc_z_freq_1.0_Hz_ws_40', 'acc_x_freq_0.2_Hz_ws_40',
'acc_y_freq_1.9_Hz_ws_40', 'gyr_x_temp_mean_ws_1200',
'acc_z_freq_1.9_Hz_ws_40', 'acc_x_temp_std_ws_120',
'gyr_z_temp_std_ws_120', 'gyr_y_freq_1.5_Hz_ws_40',
'gyr_z_temp_mean_ws_120', 'gyr_x_freq_0.0_Hz_ws_40',
'acc_z_freq_0.6_Hz_ws_40'
]
DataViz.plot_xy(x=[range(1, N_FORWARD_SELECTION + 1)],
y=[selected_features],
xlabel='number of features',
ylabel='accuracy')
print('feature selection finished for %s' % file)
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
possible_feature_sets = [
basic_features, features_2, features_3, features_4, selected_features
]
feature_names = [
'Basic features', 'Features with time', 'Features with frequency',
'Features with cluster', 'Selected features'
]
# with shelve.open('temp/shelve.out', 'n') as f:
# for key in dir():
# try:
# f[key] = globals()[key]
# except:
# print('ERROR shelving: {0}'.format(key))
N_KCV_REPEATS = 1
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
print(datetime.now())
print('possible feature sets', i)
log(['Features %d' % i])
selected_train_X = train_X[possible_feature_sets[i]]
selected_test_X = test_X[possible_feature_sets[i]]
# First we run our non deterministic classifiers a number of times to average their score.
performance_tr_rf = 0
performance_te_rf = 0
for repeat in range(0, N_KCV_REPEATS):
print(datetime.now())
print('\nRepeat', repeat)
print('Random Forest')
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.random_forest(
selected_train_X,
train_y,
selected_test_X,
gridsearch=True,
print_model_details=True)
test_cm = eval.confusion_matrix(test_y, class_test_y,
class_train_prob_y.columns)
DataViz.plot_confusion_matrix(test_cm,
class_train_prob_y.columns,
normalize=False)
performance_tr_rf += eval.accuracy(train_y, class_train_y)
performance_te_rf += eval.accuracy(test_y, class_test_y)
print(datetime.now())
overall_performance_tr_rf = performance_tr_rf / N_KCV_REPEATS
overall_performance_te_rf = performance_te_rf / N_KCV_REPEATS
log([
'RF' + ' train acc: %f' % performance_te_rf +
' test acc: %f' % performance_te_rf
])
# And we run our deterministic classifiers:
print('decision tree')
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.decision_tree(
selected_train_X,
train_y,
selected_test_X,
gridsearch=True,
print_model_details=True)
performance_tr_dt = eval.accuracy(train_y, class_train_y)
performance_te_dt = eval.accuracy(test_y, class_test_y)
test_cm = eval.confusion_matrix(test_y, class_test_y,
class_train_prob_y.columns)
DataViz.plot_confusion_matrix(test_cm,
class_train_prob_y.columns,
normalize=False)
log([
'DT' + ' train acc: %f' % performance_tr_dt +
' test acc: %f' % performance_te_dt
])
scores_with_sd = util.print_table_row_performances(
feature_names[i], len(selected_train_X.index),
len(selected_test_X.index), [
(overall_performance_tr_rf, overall_performance_te_rf),
(performance_tr_dt, performance_te_dt),
])
scores_over_all_algs.append(scores_with_sd)
DataViz.plot_performances_classification(['RF', 'DT'], feature_names,
scores_over_all_algs)
print(datetime.now())
# xlabel='number of features', ylabel='accuracy')
# Based on the plot we select the top 10 features (note: slightly different compared to Python 2, we use
# those feartures here).
selected_features = [
'rotationRate.z_temp_std_ws_180',
'userAcceleration.z_temp_std_ws_180',
'gravity.x_freq_0.0_Hz_ws_100',
'userAcceleration.y_freq_0.0_Hz_ws_100',
'gravity.x_freq_2.7_Hz_ws_100',
]
# Let us first study the impact of regularization and model complexity: does regularization prevent overfitting?
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
reg_parameters = [0.0001, 0.001, 0.01, 0.1, 1, 10]
performance_training = []
performance_test = []
# We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
# N_REPEATS_NN = 20
#
# for reg_param in reg_parameters:
# performance_tr = 0
# performance_te = 0
# for i in range(0, N_REPEATS_NN):
# class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(
# train_X, train_y,
# Based on the plot we select the top 10 features.
selected_features_with_NB = ['acc_x_temp_MAD_ws_120', 'mag_z_freq_0.0_Hz_ws_40', 'mag_y_freq_0.0_Hz_ws_40',
'lin_acc_y_freq_0.0_Hz_ws_40', 'pca_7_temp_mean_ws_120', 'acc_z_temp_std_ws_120', 'acc_y',
'pca_9_temp_MAD_ws_120', 'mag_x_freq_0.1_Hz_ws_40', 'mag_z_freq_1.1_Hz_ws_40']
selected_features_with_KNN = ['mag_y_freq_0.8_Hz_ws_40', 'pca_7_temp_std_ws_120', 'mag_x_max_freq',
'gyr_z_freq_2.0_Hz_ws_40', 'gyr_y_freq_0.0_Hz_ws_40', 'mag_z_freq_1.5_Hz_ws_40',
'acc_z_temp_MAD_ws_120', 'acc_y_temp_kurtosis_ws_120', 'mag_x_freq_1.2_Hz_ws_40',
'lin_acc_y_freq_1.8_Hz_ws_40']
selected_features_with_DT = ['acc_z_freq_0.0_Hz_ws_40', 'loc_height_temp_mean_ws_120', 'pca_4_temp_kurtosis_ws_120',
'lin_acc_y_temp_kurtosis_ws_120', 'pca_1_temp_kurtosis_ws_120', 'acc_z_temp_MAD_ws_120',
'mag_x_freq_1.2_Hz_ws_40', 'gyr_z_freq_2.0_Hz_ws_40', 'acc_y_temp_kurtosis_ws_120',
'lin_acc_y_freq_0.6_Hz_ws_40']
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
# possible_feature_sets = [basic_features, features_after_outliers_and_imputation, features_after_domain_features,
# features_after_cluster_features, selected_features_with_DT, selected_features_with_NB]
# feature_names = ['initial set', 'After imputation', 'With Domain features', 'With cluster features', 'Selected features DT', 'Selected features NB']
feature_set_axel = [basic_features, features_after_outliers_and_imputation]
feature_names_axel = ['initial set', 'After imputation']
feature_set_hasine = [features_after_domain_features, features_after_cluster_features]
feature_names_hasine = ['With Domain features', 'With cluster features']
feature_set_kim = [selected_features_with_DT, selected_features_with_NB]
feature_names_kim = ['Selected features DT', 'Selected features NB']
'mag_y_freq_0.8_Hz_ws_40', 'pca_7_temp_std_ws_120', 'mag_x_max_freq',
'gyr_z_freq_2.0_Hz_ws_40', 'gyr_y_freq_0.0_Hz_ws_40',
'mag_z_freq_1.5_Hz_ws_40', 'acc_z_temp_MAD_ws_120',
'acc_y_temp_kurtosis_ws_120', 'mag_x_freq_1.2_Hz_ws_40',
'lin_acc_y_freq_1.8_Hz_ws_40'
]
selected_features_with_DT = [
'acc_z_freq_0.0_Hz_ws_40', 'loc_height_temp_mean_ws_120',
'pca_4_temp_kurtosis_ws_120', 'lin_acc_y_temp_kurtosis_ws_120',
'pca_1_temp_kurtosis_ws_120', 'acc_z_temp_MAD_ws_120',
'mag_x_freq_1.2_Hz_ws_40', 'gyr_z_freq_2.0_Hz_ws_40',
'acc_y_temp_kurtosis_ws_120', 'lin_acc_y_freq_0.6_Hz_ws_40'
]
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
possible_feature_sets = [
basic_features, features_after_outliers_and_imputation,
features_after_domain_features, features_after_cluster_features,
selected_features_with_DT, selected_features_with_NB
]
feature_names = [
'initial set', 'After imputation', 'With Domain features',
'With cluster features', 'Selected features DT', 'Selected features NB'
]
repeats = 3
scores_over_all_algs = []
plot.plot(range(1, 26), ordered_scores)
plot.xlabel('number of features')
plot.ylabel('accuracy')
plot.show()
# Based on the plot we select the top 10 features.
selected_features = features_after_chapter_5
# ['acc_phone_y_freq_0.0_Hz_ws_40', 'press_phone_pressure_temp_mean_ws_120', 'gyr_phone_x_temp_std_ws_120',
# 'mag_watch_y_pse', 'mag_phone_z_max_freq', 'gyr_watch_y_freq_weighted', 'gyr_phone_y_freq_1.0_Hz_ws_40',
# 'acc_phone_x_freq_1.9_Hz_ws_40', 'mag_watch_z_freq_0.9_Hz_ws_40', 'acc_watch_y_freq_0.5_Hz_ws_40']
# Let us first study the impact of regularization and model complexity: does regularization prevent overfitting?
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
reg_parameters = [0.0001, 0.001, 0.01, 0.1, 1, 10]
performance_training = []
performance_test = []
# We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
repeats = 20
for reg_param in reg_parameters:
performance_tr = 0
performance_te = 0
for i in range(0, repeats):
class_train_y, class_test_y, class_train_prob_y, class_test_prob_y = learner.feedforward_neural_network(train_X, train_y,
# Based on the plot we select the top 10 features.
selected_features_with_NB = ['acc_x_temp_MAD_ws_120', 'mag_z_freq_0.0_Hz_ws_40', 'mag_y_freq_0.0_Hz_ws_40',
'lin_acc_y_freq_0.0_Hz_ws_40', 'pca_7_temp_mean_ws_120', 'acc_z_temp_std_ws_120', 'acc_y',
'pca_9_temp_MAD_ws_120', 'mag_x_freq_0.1_Hz_ws_40', 'mag_z_freq_1.1_Hz_ws_40']
selected_features_with_KNN = ['mag_y_freq_0.8_Hz_ws_40', 'pca_7_temp_std_ws_120', 'mag_x_max_freq',
'gyr_z_freq_2.0_Hz_ws_40', 'gyr_y_freq_0.0_Hz_ws_40', 'mag_z_freq_1.5_Hz_ws_40',
'acc_z_temp_MAD_ws_120', 'acc_y_temp_kurtosis_ws_120', 'mag_x_freq_1.2_Hz_ws_40',
'lin_acc_y_freq_1.8_Hz_ws_40']
selected_features_with_DT = ['acc_z_freq_0.0_Hz_ws_40', 'loc_height_temp_mean_ws_120', 'pca_4_temp_kurtosis_ws_120',
'lin_acc_y_temp_kurtosis_ws_120', 'pca_1_temp_kurtosis_ws_120', 'acc_z_temp_MAD_ws_120',
'mag_x_freq_1.2_Hz_ws_40', 'gyr_z_freq_2.0_Hz_ws_40', 'acc_y_temp_kurtosis_ws_120',
'lin_acc_y_freq_0.6_Hz_ws_40']
learner = ClassificationAlgorithms()
eval = ClassificationEvaluation()
possible_feature_sets = [basic_features, features_after_outliers_and_imputation, features_after_domain_features,
features_after_cluster_features, selected_features_with_DT, selected_features_with_NB]
feature_names = ['initial set', 'After imputation', 'With Domain features', 'With cluster features', 'Selected features DT', 'Selected features NB']
repeats = 3
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
#print "working on feature set", feature_names[i]
selected_train_X = train_X[possible_feature_sets[i]]
selected_test_X = test_X[possible_feature_sets[i]]
