except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
if not os.path.exists(export_tree_path):
os.makedirs(export_tree_path)
dataset.index = dataset.index.to_datetime()
# Let us consider our first task, namely the prediction of the label. We consider this as a non-temporal task.
# We create a single column with the categorical attribute representing our class. Furthermore, we use 70% of our data
# for training and the remaining 30% as an independent test set. We select the sets based on stratified sampling. We remove
# cases where we do not know the label.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_classification(dataset, ['label'], 'like', 0.7, filter=True, temporal=False)
#train_X, test_X, train_y, test_y = prepare.split_single_dataset_classification(dataset, ['label'], 'like', 0.01, filter=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 that we will consider:
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))]
'''
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles/'
try:
dataset = pd.read_csv(dataset_path + 'chapter5_our_result.csv',
index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
# Let us consider our second task, namely the prediction of the Azimuth. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset,
'Azimuth',
'2019-06-14 12:06:30',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2019-06-14 12:16:02',
'2019-06-14 12:20:56')
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
print 'Training set length is: ', len(train_X.index)
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 second task, namely the prediction of the heart rate. Therefore create a dataset with the heart
# rate as target and split using timestamps, because this is considered as a temporal task.
print('\n- - - Loading dataset - - -')
prepare = PrepareDatasetForLearning()
learner = RegressionAlgorithms()
evaluation = RegressionEvaluation()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(dataset, 'hr_watch_rate',
'2016-02-08 18:28:56',
'2016-02-08 19:34:07',
'2016-02-08 20:07:50')
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',
'labelOnTable', 'labelSitting', 'labelWashingHands', 'labelWalking', 'labelStanding',
'labelDriving',
'labelEating', 'labelRunning', '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 and 'hr_watch' not 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 Pearson correlations and see whether features can be selected based on them
fs = FeatureSelectionRegression()
print('\n- - - Running feature selection - - -')
features, correlations = fs.pearson_selection(10, train_X[features_after_chapter_5], train_y)
util.print_pearson_correlations(correlations)
# Select the 10 features with the highest correlation
selected_features = ['temp_pattern_labelOnTable', 'labelOnTable', 'temp_pattern_labelOnTable(b)labelOnTable',
'pca_2_temp_mean_ws_120', 'pca_1_temp_mean_ws_120', 'acc_watch_y_temp_mean_ws_120', 'pca_2',
'acc_phone_z_temp_mean_ws_120', 'gyr_watch_y_pse', 'gyr_watch_x_pse']
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']
if FLAGS.mode == 'overall' or FLAGS.mode == 'all':
print('\n- - - Running test of all different regression algorithms - - -')
# First study the importance of the parameter settings. Therefore repeat the experiment a number of times to get
# a bit more robust data as the initialization of e.g. the NN is random
REPEATS = FLAGS.repeats
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]]
performance_tr_nn, performance_tr_nn_std = 0, 0
performance_tr_rf, performance_tr_rf_std = 0, 0
performance_te_nn, performance_te_nn_std = 0, 0
performance_te_rf, performance_te_rf_std = 0, 0
# First run non deterministic classifiers a number of times to average their score
for repeat in range(0, REPEATS):
print(f'Training NeuralNetwork run {repeat + 1}/{REPEATS} ... ')
regr_train_y, regr_test_y = learner.\
feedforward_neural_network(selected_train_X, train_y, selected_test_X, gridsearch=True)
mean_tr, std_tr = evaluation.mean_squared_error_with_std(train_y, regr_train_y)
mean_te, std_te = evaluation.mean_squared_error_with_std(test_y, regr_test_y)
performance_tr_nn += mean_tr
performance_tr_nn_std += std_tr
performance_te_nn += mean_te
performance_te_nn_std += std_te
print(f'Training RandomForest run {repeat + 1}/{REPEATS} ... ')
regr_train_y, regr_test_y = learner.random_forest(selected_train_X, train_y, selected_test_X,
gridsearch=True)
mean_tr, std_tr = evaluation.mean_squared_error_with_std(train_y, regr_train_y)
mean_te, std_te = evaluation.mean_squared_error_with_std(test_y, regr_test_y)
performance_tr_rf += mean_tr
performance_tr_rf_std += std_tr
performance_te_rf += mean_te
performance_te_rf_std += std_te
overall_performance_tr_nn = performance_tr_nn / REPEATS
overall_performance_tr_nn_std = performance_tr_nn_std / REPEATS
overall_performance_te_nn = performance_te_nn / REPEATS
overall_performance_te_nn_std = performance_te_nn_std / REPEATS
overall_performance_tr_rf = performance_tr_rf / REPEATS
overall_performance_tr_rf_std = performance_tr_rf_std / REPEATS
overall_performance_te_rf = performance_te_rf / REPEATS
overall_performance_te_rf_std = performance_te_rf_std / REPEATS
# Run deterministic algorithms:
print("Support Vector Regressor run 1/1 ... ")
# Convergence of the SVR does not always occur (even adjusting tolerance and iterations does not help)
regr_train_y, regr_test_y = learner.\
support_vector_regression_without_kernel(selected_train_X, train_y, selected_test_X, gridsearch=False)
mean_tr, std_tr = evaluation.mean_squared_error_with_std(train_y, regr_train_y)
mean_te, std_te = evaluation.mean_squared_error_with_std(test_y, regr_test_y)
performance_tr_svm = mean_tr
performance_tr_svm_std = std_tr
performance_te_svm = mean_te
performance_te_svm_std = std_te
print("Training Nearest Neighbor run 1/1 ... ")
regr_train_y, regr_test_y = learner.k_nearest_neighbor(selected_train_X, train_y, selected_test_X,
gridsearch=True)
mean_tr, std_tr = evaluation.mean_squared_error_with_std(train_y, regr_train_y)
mean_te, std_te = evaluation.mean_squared_error_with_std(test_y, regr_test_y)
performance_tr_knn = mean_tr
performance_tr_knn_std = std_tr
performance_te_knn = mean_te
performance_te_knn_std = std_te
print("Training Decision Tree run 1/1 ... ")
regr_train_y, regr_test_y = learner.\
decision_tree(selected_train_X, train_y, selected_test_X, gridsearch=True,
export_tree_path=EXPORT_TREE_PATH)
mean_tr, std_tr = evaluation.mean_squared_error_with_std(train_y, regr_train_y)
mean_te, std_te = evaluation.mean_squared_error_with_std(test_y, regr_test_y)
performance_tr_dt = mean_tr
performance_tr_dt_std = std_tr
performance_te_dt = mean_te
performance_te_dt_std = std_te
scores_with_sd = [(overall_performance_tr_nn, overall_performance_tr_nn_std, overall_performance_te_nn,
overall_performance_te_nn_std),
(overall_performance_tr_rf, overall_performance_tr_rf_std, overall_performance_te_rf,
overall_performance_te_rf_std),
(performance_tr_svm, performance_tr_svm_std, performance_te_svm, performance_te_svm_std),
(performance_tr_knn, performance_tr_knn_std, performance_te_knn, performance_te_knn_std),
(performance_tr_dt, performance_tr_dt_std, performance_te_dt, performance_te_dt_std)]
util.print_table_row_performances_regression(feature_names[i], scores_with_sd)
scores_over_all_algs.append(scores_with_sd)
# Plot the results
DataViz.plot_performances_regression(['NN', 'RF', 'SVM', 'KNN', 'DT'], feature_names, scores_over_all_algs)
if FLAGS.mode == 'detail' or FLAGS.mode == 'all':
print('\n- - - Running visualization of results - - -')
regr_train_y, regr_test_y = learner.random_forest(train_X[features_after_chapter_5], train_y,
test_X[features_after_chapter_5], gridsearch=False,
print_model_details=True)
DataViz.plot_numerical_prediction_versus_real(train_X.index, train_y, regr_train_y, test_X.index, test_y,
regr_test_y, 'heart rate')
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)
DataViz = VisualizeDataset()
# Read the result from the previous chapter, and make sure the index is of the type datetime.
dataset_path = './intermediate_datafiles/'
try:
dataset = pd.read_csv(dataset_path + 'chapter5_result.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(dataset, 'hr_watch_rate', '2016-02-08 18:29:56',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2016-02-08 19:34:07', '2016-02-08 20:07:50')
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
DATA_PATH = Path('./intermediate_datafiles/Crowdsignal/')
DATASET_FNAME = 'chapter5_result.csv'
DataViz = VisualizeDataset(__file__)
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FNAME, index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = pd.to_datetime(dataset.index)
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset,
'hr_watch_rate',
'2016-02-08 18:29:56',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2016-02-08 19:34:07',
'2016-02-08 20:07:50')
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features that we will consider:
print('Training set length is: ', len(train_X.index))
try:
dataset = pd.read_csv(dataset_path + 'chapter5_result-own.csv',
index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset = pd.read_csv(dataset_path + 'chapter5_result-own.csv', index_col=0)
dataset.index = dataset.index.to_datetime()
if not os.path.exists(export_tree_path):
os.makedirs(export_tree_path)
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset, 'light_phone_lux', '2016-02-08 18:28:56', '2016-02-08 19:34:07',
'2016-02-08 20:07:50')
# '2016-02-08 18:28:58','2016-02-08 18:28:59')
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
basic_features = [
'acc_phone_x', 'acc_phone_y', 'acc_phone_z', 'gyr_phone_x', 'gyr_phone_y',
'gyr_phone_z', 'labelOnTable', 'labelSitting', 'labelSmoking',
'labelWalkingStairs', 'labelStandingInElevator', 'mag_phone_x',
from Operations import *
# Set up file names and locations.
DATA_PATH = Path('./intermediate_datafiles/')
DATASET_FNAME = sys.argv[1] if len(sys.argv) > 1 else 'chapter2_result.csv'
RESULT_FNAME = sys.argv[2] if len(
sys.argv) > 2 else 'chapter3_result_outliers.csv'
dataset = pickle.load(open('concat_no_skipping.pkl', 'rb'))
dataset = rename(dataset)
# dataset.index = pd.to_datetime(dataset.index)
DataViz = VisualizeDataset(__file__, show=False)
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
dataset = dataset.fillna(0)
print(dataset.index)
print(dataset.loc[dataset.index.values[0]])
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset, "gyr_z", dataset.index.values[0],
dataset.index.values[int(len(dataset.index) * 0.7)],
dataset.index.values[-1])
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features that we will consider:
print('Training set length is: ', len(train_X.index))
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
if not os.path.exists(export_tree_path):
os.makedirs(export_tree_path)
dataset.index = dataset.index.to_datetime()
dataset = dataset.dropna()
# Let us consider our first task, namely the prediction of the label. We consider this as a non-temporal task.
# We create a single column with the categorical attribute representing our class. Furthermore, we use 70% of our data
# for training and the remaining 30% as an independent test set. We select the sets based on stratified sampling. We remove
# cases where we do not know the label.
prepare = PrepareDatasetForLearning()
exact_labels = [
'labelOnTable', 'labelSitting', 'labelWashingHands', 'labelWalking',
'labelStanding', 'labelDriving', 'labelEating', 'labelRunning'
]
sum_values = dataset[exact_labels].sum(axis=1)
# Create a new 'class' column and set the value to the default class.
dataset['class'] = 'undefined'
for i in range(0, len(dataset.index)):
# If we have exactly one true class column, we can assign that value,
# otherwise we keep the default class.
first = True
for label in exact_labels:
if dataset.ix[i, label] == 1:
if first:
try:
dataset = pd.read_csv(dataset_path + 'mydata_chapter5_result.csv', index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset = dataset.dropna(axis=0,how = 'any',inplace=False)
dataset.index = dataset.index.to_datetime()
# print(dataset.isnull().sum())
# exit(0)
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(dataset, 'gyr_phone_x', '2017-06-09 10:20:28',
'2017-06-09 11:18:27', '2017-06-09 11:43:23')
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
basic_features = ['acc_phone_x','acc_phone_y','acc_phone_z','gyr_phone_y','gyr_phone_z',
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())
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FNAME, index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
random_state = 2020
dataset.index = pd.to_datetime(dataset.index)
# dataset["hr_watch_rate"] = np.random.randint(60,120, size=len(dataset))
dataset = dataset.fillna(0)
# print(dataset["hr_watch_rate"])
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset,
'acc_phone_x',
'2020-06-05 13:11:27',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2020-06-05 13:43:26',
'2020-06-05 13:55:40')
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features that we will consider:
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
DATA_PATH = Path('./intermediate_datafiles/')
DATASET_FNAME = 'chapter5_result_own.csv'
DataViz = VisualizeDataset(__file__)
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FNAME, index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = pd.to_datetime(dataset.index)
# Let us consider our second task, namely the prediction of the heart rate. We consider this as a temporal task.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset,
'gyr_phone_z',
'2020-06-02 13:11:36',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2020-06-02 13:52:51',
'2020-06-02 14:13:28')
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features that we will consider:
print('Training set length is: ', len(train_X.index))
def main():
# Read the result from the previous chapter and convert the index to datetime
try:
dataset = pd.read_csv(DATA_PATH / DATASET_FNAME, 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 second task, namely the prediction of the heart rate. Therefore create a dataset with the heart
# rate as target and split using timestamps, because this is considered as a temporal task
print('\n- - - Loading dataset - - -')
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset, 'hr_watch_rate', '2016-02-08 18:29:56', '2016-02-08 19:34:07',
'2016-02-08 20:07:50')
print('Training set length is: ', len(train_X.index))
print('Test set length is: ', len(test_X.index))
# Select subsets of the features
print('\n- - - 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',
'labelOnTable', 'labelSitting', 'labelWashingHands', 'labelWalking',
'labelStanding', 'labelDriving', 'labelEating', 'labelRunning',
'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 and 'hr_watch' not 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))
selected_features = [
'temp_pattern_labelOnTable', 'labelOnTable',
'temp_pattern_labelOnTable(b)labelOnTable', 'cluster',
'pca_1_temp_mean_ws_120', 'pca_2_temp_mean_ws_120', 'pca_2',
'acc_watch_y_temp_mean_ws_120', 'gyr_watch_y_pse', 'gyr_watch_x_pse'
]
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'
]
if FLAGS.mode == 'correlation' or FLAGS.mode == 'all':
# First study whether the time series is stationary and what the autocorrelations are
adfuller(dataset['hr_watch_rate'], autolag='AIC')
plt.Figure()
autocorrelation_plot(dataset['hr_watch_rate'])
DataViz.save(plt)
plt.show()
# Now focus on the learning part
learner = TemporalRegressionAlgorithms()
evaluate = RegressionEvaluation()
if FLAGS.mode == 'overall' or FLAGS.mode == 'all':
# Repeat the experiment a number of times to get a bit more robust data as the initialization of e.g. the NN is
# random
repeats = FLAGS.repeats
# Set a washout time to give the NN's the time to stabilize (so don't compute the error during the washout time)
washout_time = FLAGS.washout
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
print(f'Evaluating for features {possible_feature_sets[i]}')
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_res, performance_tr_res_std = 0, 0
performance_te_res, performance_te_res_std = 0, 0
performance_tr_rnn, performance_tr_rnn_std = 0, 0
performance_te_rnn, performance_te_rnn_std = 0, 0
for repeat in range(0, repeats):
print(f'--- run {repeat} ---')
regr_train_y, regr_test_y = learner.reservoir_computing(
selected_train_X,
train_y,
selected_test_X,
test_y,
gridsearch=True,
per_time_step=False)
mean_tr, std_tr = evaluate.mean_squared_error_with_std(
train_y.iloc[washout_time:, ],
regr_train_y.iloc[washout_time:, ])
mean_te, std_te = evaluate.mean_squared_error_with_std(
test_y.iloc[washout_time:, ],
regr_test_y.iloc[washout_time:, ])
performance_tr_res += mean_tr
performance_tr_res_std += std_tr
performance_te_res += mean_te
performance_te_res_std += std_te
regr_train_y, regr_test_y = learner.recurrent_neural_network(
selected_train_X,
train_y,
selected_test_X,
test_y,
gridsearch=True)
mean_tr, std_tr = evaluate.mean_squared_error_with_std(
train_y.iloc[washout_time:, ],
regr_train_y.iloc[washout_time:, ])
mean_te, std_te = evaluate.mean_squared_error_with_std(
test_y.iloc[washout_time:, ],
regr_test_y.iloc[washout_time:, ])
performance_tr_rnn += mean_tr
performance_tr_rnn_std += std_tr
performance_te_rnn += mean_te
performance_te_rnn_std += std_te
# Only apply the time series in case of the basis features
if feature_names[i] == 'initial set':
regr_train_y, regr_test_y = learner.time_series(
selected_train_X,
train_y,
selected_test_X,
test_y,
gridsearch=True)
mean_tr, std_tr = evaluate.mean_squared_error_with_std(
train_y.iloc[washout_time:, ],
regr_train_y.iloc[washout_time:, ])
mean_te, std_te = evaluate.mean_squared_error_with_std(
test_y.iloc[washout_time:, ],
regr_test_y.iloc[washout_time:, ])
overall_performance_tr_ts = mean_tr
overall_performance_tr_ts_std = std_tr
overall_performance_te_ts = mean_te
overall_performance_te_ts_std = std_te
else:
overall_performance_tr_ts = 0
overall_performance_tr_ts_std = 0
overall_performance_te_ts = 0
overall_performance_te_ts_std = 0
overall_performance_tr_res = performance_tr_res / repeats
overall_performance_tr_res_std = performance_tr_res_std / repeats
overall_performance_te_res = performance_te_res / repeats
overall_performance_te_res_std = performance_te_res_std / repeats
overall_performance_tr_rnn = performance_tr_rnn / repeats
overall_performance_tr_rnn_std = performance_tr_rnn_std / repeats
overall_performance_te_rnn = performance_te_rnn / repeats
overall_performance_te_rnn_std = performance_te_rnn_std / repeats
scores_with_sd = [
(overall_performance_tr_res, overall_performance_tr_res_std,
overall_performance_te_res, overall_performance_te_res_std),
(overall_performance_tr_rnn, overall_performance_tr_rnn_std,
overall_performance_te_rnn, overall_performance_te_rnn_std),
(overall_performance_tr_ts, overall_performance_tr_ts_std,
overall_performance_te_ts, overall_performance_te_ts_std)
]
util.print_table_row_performances_regression(
feature_names[i], scores_with_sd)
scores_over_all_algs.append(scores_with_sd)
DataViz.plot_performances_regression(
['Reservoir', 'RNN', 'Time series'], feature_names,
scores_over_all_algs)
if FLAGS.mode == 'detail' or FLAGS.mode == 'all':
regr_train_y, regr_test_y = learner.reservoir_computing(
train_X[features_after_chapter_5],
train_y,
test_X[features_after_chapter_5],
test_y,
gridsearch=False)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y, regr_train_y['hr_watch_rate'],
test_X.index, test_y, regr_test_y['hr_watch_rate'], 'heart rate')
regr_train_y, regr_test_y = learner.recurrent_neural_network(
train_X[basic_features],
train_y,
test_X[basic_features],
test_y,
gridsearch=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y, regr_train_y['hr_watch_rate'],
test_X.index, test_y, regr_test_y['hr_watch_rate'], 'heart rate')
regr_train_y, regr_test_y = learner.time_series(
train_X[basic_features],
train_y,
test_X[basic_features],
test_y,
gridsearch=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y, regr_train_y['hr_watch_rate'],
test_X.index, test_y, regr_test_y['hr_watch_rate'], 'heart rate')
if FLAGS.mode == 'dynamical' or FLAGS.mode == 'all':
# And now some example code for using the dynamical systems model with parameter tuning (note: focus on
# predicting accelerometer data):
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression(
copy.deepcopy(dataset), ['acc_phone_x', 'acc_phone_y'],
0.9,
filter_data=False,
temporal=True)
output_sets = learner. \
dynamical_systems_model_nsga_2(train_X, train_y, test_X, test_y,
['self.acc_phone_x', 'self.acc_phone_y', 'self.acc_phone_z'],
['self.a * self.acc_phone_x + self.b * self.acc_phone_y',
'self.c * self.acc_phone_y + self.d * self.acc_phone_z',
'self.e * self.acc_phone_x + self.f * self.acc_phone_z'],
['self.acc_phone_x', 'self.acc_phone_y'],
['self.a', 'self.b', 'self.c', 'self.d', 'self.e', 'self.f'],
pop_size=10, max_generations=10, per_time_step=True)
DataViz.plot_pareto_front(output_sets)
DataViz.plot_numerical_prediction_versus_real_dynsys_mo(
train_X.index, train_y, test_X.index, test_y, output_sets, 0,
'acc_phone_x')
regr_train_y, regr_test_y = learner. \
dynamical_systems_model_ga(train_X, train_y, test_X, test_y,
['self.acc_phone_x', 'self.acc_phone_y', 'self.acc_phone_z'],
['self.a * self.acc_phone_x + self.b * self.acc_phone_y',
'self.c * self.acc_phone_y + self.d * self.acc_phone_z',
'self.e * self.acc_phone_x + self.f * self.acc_phone_z'],
['self.acc_phone_x', 'self.acc_phone_y'],
['self.a', 'self.b', 'self.c', 'self.d', 'self.e', 'self.f'],
pop_size=5, max_generations=10, per_time_step=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y['acc_phone_x'], regr_train_y['acc_phone_x'],
test_X.index, test_y['acc_phone_x'], regr_test_y['acc_phone_x'],
'acc_phone_x')
regr_train_y, regr_test_y = learner. \
dynamical_systems_model_sa(train_X, train_y, test_X, test_y,
['self.acc_phone_x', 'self.acc_phone_y', 'self.acc_phone_z'],
['self.a * self.acc_phone_x + self.b * self.acc_phone_y',
'self.c * self.acc_phone_y + self.d * self.acc_phone_z',
'self.e * self.acc_phone_x + self.f * self.acc_phone_z'],
['self.acc_phone_x', 'self.acc_phone_y'],
['self.a', 'self.b', 'self.c', 'self.d', 'self.e', 'self.f'],
max_generations=10, per_time_step=True)
DataViz.plot_numerical_prediction_versus_real(
train_X.index, train_y['acc_phone_x'], regr_train_y['acc_phone_x'],
test_X.index, test_y['acc_phone_x'], regr_test_y['acc_phone_x'],
'acc_phone_x')
flattened_values[columnname +"_max"] = (np.max(values))
#flattened_values = flattened_values.join(np.max(transformation))
#flattened_values = np.append(flattened_values, np.argmax(transformation))
#flattened_values = np.append(flattened_values, np.max(transformation))
df = pd.DataFrame(data=flattened_values)
df['class'] = str(label)
frames.append(df)
result = pd.concat(frames)
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))
index_col=0)
except IOError as e:
print('File not found, try to run previous crowdsignals scripts first!')
raise e
dataset.index = dataset.index.to_datetime()
dataset = dataset.dropna()
del dataset['silhouette']
# Let us consider our first task, namely the prediction of the label. We consider this as a non-temporal task.
# We create a single column with the categorical attribute representing our class. Furthermore, we use 70% of our data
# for training and the remaining 30% as an independent test set. We select the sets based on stratified sampling. We remove
# cases where we do not know the label.
prepare = PrepareDatasetForLearning()
train_X, test_X, train_y, test_y = prepare.split_single_dataset_regression_by_time(
dataset,
'acc_phone_x',
'2017-06-13 22:21:02',
# '2016-02-08 18:29:58','2016-02-08 18:29:59')
'2017-06-13 23:40:47',
'2017-06-14 00:22:24')
print 'Training set length is: ', len(train_X.index)
print 'Test set length is: ', len(test_X.index)
# Select subsets of the features that we will consider:
print 'Training set length is: ', len(train_X.index)
