def forward_selection(max_features, X_train, y_train):
ordered_features = []
ordered_scores = []
# Start with no features.
selected_features = []
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
prev_best_perf = sys.float_info.max
# 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 = sys.float_info.max
best_feature = ''
# 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 mse of a decision tree learner if we were to add
# the feature.
regr_train_y, regr_test_y = learner.recurrent_neural_network(
X_train, y_train, X_train, y_train, gridsearch=False)
perf, std_tr = eval.mean_squared_error_with_std(
train_y.ix[10:, ], regr_train_y.ix[10:, ])
# If the performance is better than what we have seen so far (we aim for low mse)
# 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
]
feature_names = [
'initial set', 'Chapter 3', 'Chapter 4', 'Chapter 5', 'Selected features'
]
# Let us first study whether the time series is stationary and what the autocorrelations are.
dftest = adfuller(dataset['Azimuth'], autolag='AIC')
print dftest
autocorrelation_plot(dataset['Azimuth'])
plot.show()
# Now let us focus on the learning part.
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
# We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
repeats = 5
# we set a washout time to give the NN's the time to stabilize. We do not compute the error during the washout time.
washout_time = 10
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
selected_train_X = train_X[possible_feature_sets[i]]
feature_names = [
'initial set', 'Selected features'
] #'Chapter 3', 'Chapter 4', 'Chapter 5', 'Selected features']
# Let us first study whether the time series is stationary and what the autocorrelations are.
dftest = adfuller(dataset['hr_watch_rate'], autolag='AIC')
plt.Figure()
autocorrelation_plot(dataset['hr_watch_rate'])
DataViz.save(plt)
plt.show()
# Now let us focus on the learning part.
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
# We 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 = 10
# we set a washout time to give the NN's the time to stabilize. We do not compute the error during the washout time.
washout_time = 10
scores_over_all_algs = []
for i in range(0, len(possible_feature_sets)):
print(f'Evaluating for features {possible_feature_sets[i]}')
]
feature_names = [
'initial set', 'Chapter 3', 'Chapter 4', 'Chapter 5', 'Selected features'
]
# Let us first study whether the time series is stationary and what the autocorrelations are.
# dftest = adfuller(dataset['hr_watch_rate'], autolag='AIC')
# print dftest
#
# autocorrelation_plot(dataset['hr_watch_rate'])
# plot.show()
# Now let us focus on the learning part.
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
# # We repeat the experiment a number of times to get a bit more robust data as the initialization of the NN is random.
#
# repeats = 5
#
# # we set a washout time to give the NN's the time to stabilize. We do not compute the error during the washout time.
#
# washout_time = 10
#
# scores_over_all_algs = []
#
# for i in range(0, len(possible_feature_sets)):
#
# selected_train_X = train_X[possible_feature_sets[i]]
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']
# Let us first study whether the time series is stationary and what the autocorrelations are.
dftest = adfuller(dataset['hr_watch_rate'], autolag='AIC')
print dftest
autocorrelation_plot(dataset['hr_watch_rate'])
plot.show()
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
['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']
for i in range(0, len(possible_feature_sets)):
selected_train_X = train_X[possible_feature_sets[4]]
selected_test_X = test_X[possible_feature_sets[4]]
print(selected_train_X.columns)
# a list of columns the model addresses (i.e. the states), the string should be preceded by 'self.' in order for the approach to work.
# a list of equations to derive the specified states, again using 'self.' preceding all parameters and columns names.
# a list of targets (a subset of the columns) (again with 'self.')
feature_names = [
'initial set', 'Chapter 3', 'Chapter 4', 'Chapter 5', 'Selected features'
]
# Let us first study whether the time series is stationary and what the autocorrelations are.
dftest = adfuller(dataset['acc_phone_x'], autolag='AIC')
plt.Figure()
autocorrelation_plot(dataset['acc_phone_x'])
DataViz.save(plt)
plt.show()
# Now let us focus on the learning part.
learner = TemporalRegressionAlgorithms()
eval = RegressionEvaluation()
# We 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 = 3
# we set a washout time to give the NN's the time to stabilize. We do not compute the error during the washout time.
washout_time = 5
scores_over_all_algs = []
# for i in range(0, len(possible_feature_sets)):
#
# print(f'Evaluating for features {possible_feature_sets[i]}')
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')