def generate_seasonal_time_series_data_with_trend(n_samples=1,
n_obs=100,
order=0,
sp=1,
model='additive'):
"""Helper function to generate time series/panel data with polynomial
trend and seasonal component"""
if sp == 1:
return generate_time_series_data_with_trend(n_instances=n_samples,
n_timepoints=n_obs,
order=order)
samples = []
for i in range(n_samples):
# coefs = np.random.normal(scale=0.01, size=(order + 1, 1))
s = generate_polynomial_series(n_obs, order)
if model == 'additive':
s[::sp] = s[::sp] + 0.1
else:
s[::sp] = s[::sp] * 1.1
index = np.arange(n_obs)
y = pd.Series(s, index=index)
samples.append(y)
X = pd.DataFrame(samples)
assert X.shape == (n_samples, n_obs)
return detabularise(X)
def transform(self, X, y=None):
"""
Transform X, transforms univariate time-series using sklearn's PCA
class
Parameters
----------
X : nested pandas DataFrame of shape [n_samples, 1]
Nested dataframe with univariate time-series in cells.
Returns
-------
Xt : pandas DataFrame
Transformed pandas DataFrame with the same number of rows and the
(potentially reduced) PCA transformed
column. Time indices of the original column are replaced with 0:(
n_components - 1).
"""
self.check_is_fitted()
X = check_X(X, enforce_univariate=True)
# Transform X using the fitted PCA
Xtab = tabularize(X)
Xpca = pd.DataFrame(data=self.pca.transform(Xtab),
index=Xtab.index,
columns=Xtab.columns[:self.pca.n_components_])
# Back-transform into time series data format
Xt = detabularise(Xpca, index=X.index)
Xt.columns = X.columns
return Xt
def generate_time_series_data_with_trend(n_samples=1, n_obs=100, order=0, coefs=None):
"""Helper function to generate time series/panel data with polynomial trend"""
samples = []
for i in range(n_samples):
s = generate_polynomial_series(n_obs, order=order, coefs=coefs)
index = np.arange(n_obs)
y = pd.Series(s, index=index)
samples.append(y)
X = pd.DataFrame(samples)
assert X.shape == (n_samples, n_obs)
return detabularise(X)
def generate_time_series_data_with_trend(
n_instances=1, n_timepoints=100, order=0, coefs=None, noise=False
):
"""Helper function to generate time series/panel data with polynomial
trend"""
samples = []
for _ in range(n_instances):
s = generate_polynomial_series(n_timepoints, order=order, coefs=coefs)
if noise:
s = s + np.random.normal(size=n_timepoints)
index = np.arange(n_timepoints)
y = pd.Series(s, index=index)
samples.append(y)
X = pd.DataFrame(samples)
assert X.shape == (n_instances, n_timepoints)
return detabularise(X)
clf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
saste_fixed = SASTEnsemble(cand_length_list=combination_list,
nb_inst_per_class=1,
random_state=None,
classifier=clf,
n_jobs=-1)
start = time.time()
saste_fixed.fit(X_train, y_train)
saste_fixed.score(X_test, y_test)
saste_fixed_time = time.time() - start
## ShapeletTransform
X_train_sktime = detabularise(pd.DataFrame(X_train))
X_test_sktime = detabularise(pd.DataFrame(X_test))
clf = RidgeClassifierCV(alphas=np.logspace(-3, 3, 10))
stc = ShapeletTransform(min_shapelet_length=min_shp_length,
max_shapelet_length=np.inf)
start = time.time()
stc.fit(X_train_sktime, y_train)
X_train_transformed = stc.transform(X_train_sktime)
clf.fit(X_train_transformed, y_train)
X_test_transformed = stc.transform(X_test_sktime)
clf.score(X_test_transformed, y_test)
def build_model(dataset,
pipeline,
experiment,
current_target='class',
test_size=0.3):
models_dir = './results/{}_{}_{}/models/'.format(dataset, pipeline,
experiment)
reports_dir = './results/{}_{}_{}/reports/'.format(dataset, pipeline,
experiment)
experiment_index_file = './results/{}_{}_{}/index.json'.format(
dataset, pipeline, experiment)
log_file = './results/{}_{}_{}/model_build.log'.format(
dataset, pipeline, experiment)
scoring = make_scorer(precision_score, zero_division=1, average='micro')
os.makedirs(models_dir, exist_ok=True)
os.makedirs(reports_dir, exist_ok=True)
# Setup logging
logger.setup(filename=log_file,
filemode='w',
root_level=logging.DEBUG,
log_level=logging.DEBUG,
logger='build_model')
index_name = 'index'
if '.' in dataset:
splits = dataset.split(".")
dataset = splits[0]
index_name = splits[1]
# Load the dataset index
dataset_index = load_dataset(dataset,
return_index=True,
index_name=index_name)
# Dynamically import the pipeline we want to use for building the model
logger.info('Start experiment: {} using {} on {} with target {}'.format(
experiment, pipeline, dataset, current_target))
reports = ReportCollection(dataset, pipeline, experiment)
for _sym, data in {'BTC': dataset_index['BTC']}.items():
try:
logger.info('Start processing: {}'.format(_sym))
features = pd.read_csv(data['csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
targets = pd.read_csv(data['target_csv'],
sep=',',
encoding='utf-8',
index_col='Date',
parse_dates=True)
# Drop columns whose values are all NaN, as well as rows with ANY nan value, then
# replace infinity values with nan so that they can later be imputed to a finite value
features = features.dropna(
axis='columns', how='all').dropna().replace([np.inf, -np.inf],
np.nan)
target = targets.loc[features.index][current_target]
#X_train, X_test, y_train, y_test = train_test_split(features, target, shuffle=False, test_size=test_size)
all_size = features.shape[0]
train_size = int(all_size * (1 - test_size))
features = detabularise(
features[[c for c in features.columns if 'close' in c]])
X_train = features.iloc[0:train_size]
y_train = target.iloc[0:train_size]
X_test = features.iloc[train_size:all_size]
y_test = target.iloc[train_size:all_size]
# Summarize distribution
logger.info("Start Grid search")
clf = ShapeletTransformClassifier(time_contract_in_mins=5)
clf.fit(X_train, y_train)
print('{} Score: {}'.format(_sym, clf.score(X_test, y_test)))
pred = clf.predict(X_test)
print(classification_report(y_test, pred))
logger.info("End Grid search")
logger.info("--- {} end ---".format(_sym))
except Exception as e:
logger.error(
"Exception while building model pipeline: {} dataset: {} symbol: {}\nException:\n{}"
.format(pipeline, dataset, _sym, e))
traceback.print_exc()
return reports
