features_after_chapter_5 = list(set().union(basic_features, pca_features,
time_features, freq_features,
cluster_features))
# datetime object containing current date and time
now3 = datetime.now()
# dd/mm/YY H:M:S
dt_string = now3.strftime("%d/%m/%Y %H:%M:%S")
print("date and time =", dt_string)
diff = now3 - now2
print('difference time', diff)
# First, let us consider the performance over a selection of features:
fs = FeatureSelectionClassification()
features, ordered_features, ordered_scores = fs.forward_selection(
N_FORWARD_SELECTION, train_X[features_after_chapter_5], train_y)
print(ordered_scores)
print(ordered_features)
DataViz.plot_xy(x=[range(1, N_FORWARD_SELECTION + 1)],
y=[ordered_scores],
xlabel='number of features',
ylabel='accuracy')
# datetime object containing current date and time
now4 = datetime.now()
# dd/mm/YY H:M:S
dt_string = now4.strftime("%d/%m/%Y %H:%M:%S")
'''
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:
fs = FeatureSelectionClassification()
'''
features, ordered_features, ordered_scores = fs.forward_selection(25, train_X[features_after_chapter_5], train_y)
print ordered_scores
print ordered_features
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',
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)
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))
]
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:
fs = FeatureSelectionClassification()
#features, ordered_features, ordered_scores = fs.forward_selection(15, train_X[list(set().union(basic_features, pca_features, time_features))], train_y)
#print ordered_scores
#print ordered_features
#print features
features = fs.backward_selection(
15, train_X[list(set().union(basic_features, pca_features,
time_features))], train_y)
print features
print '#frequency features: ', len(freq_features)
cluster_features = ['cluster']
print '#cluster features: ', len(cluster_features)
features_after_outliers_and_imputation = list(set().union(
basic_features, pca_features))
features_after_domain_features = list(set().union(basic_features, pca_features,
time_features,
freq_features))
features_after_cluster_features = list(set().union(basic_features,
pca_features, time_features,
freq_features,
cluster_features))
# First, let us consider the performance over a selection of features:
fs = FeatureSelectionClassification()
features, ordered_features, ordered_scores = fs.ax_forward_selection_naive_bayes(
20, train_X[features_after_chapter_5], train_y)
#features, ordered_features, ordered_scores = fs.backward_selection(50, train_X[features_after_chapter_5], train_y)
# features, ordered_features, ordered_scores = fs.forward_selection(20, train_X[features_after_chapter_5], train_y)
print ordered_scores
print ordered_features
plot.plot(range(1, 21), ordered_scores)
plot.xlabel('number of features')
plot.ylabel('accuracy')
plot.show()
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:
fs = FeatureSelectionClassification()
# features, ordered_features, ordered_scores = fs.forward_selection(N_FORWARD_SELECTION,
# train_X[features_after_chapter_5], train_y)
# print(ordered_scores)
# print(ordered_features)
#
# DataViz.plot_xy(x=[range(1, N_FORWARD_SELECTION + 1)], y=[ordered_scores],
# 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',
'gyr_phone_z', 'mag_phone_x', 'mag_phone_y', 'mag_phone_z',
'press_phone_Pressure'
]
pca_features = ['pca_1', 'pca_2', 'pca_3', 'pca_4']
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))
]
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:
fs = FeatureSelectionClassification()
# features, ordered_features, ordered_scores = fs.forward_selection(50, train_X[features_after_chapter_5], train_y)
features = fs.backward_selection(10, train_X[features_after_chapter_5],
train_y)
print features
exit(0)