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@winedarksea
winedarksea committed Oct 31, 2024
1 parent ebdc73a commit 52ed51b
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Showing 4 changed files with 43 additions and 59 deletions.
15 changes: 1 addition & 14 deletions autots/evaluator/auto_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,7 @@
)
from autots.models.arch import ARCH
from autots.models.matrix_var import RRVAR, MAR, TMF, LATC, DMD
from autots.models.sklearn import RollingRegression, WindowRegression, MultivariateRegression, DatepartRegression, UnivariateRegression, ComponentAnalysis, PreprocessingRegression


def create_model_id(
Expand Down Expand Up @@ -191,8 +192,6 @@ def ModelMonster(
return model

elif model == 'RollingRegression':
from autots.models.sklearn import RollingRegression

model = RollingRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand All @@ -204,8 +203,6 @@ def ModelMonster(
)
return model
elif model == 'UnivariateRegression':
from autots.models.sklearn import UnivariateRegression

model = UnivariateRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand All @@ -219,8 +216,6 @@ def ModelMonster(
return model

elif model == 'MultivariateRegression':
from autots.models.sklearn import MultivariateRegression

model = MultivariateRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand Down Expand Up @@ -316,8 +311,6 @@ def ModelMonster(
)
return model
elif model == 'WindowRegression':
from autots.models.sklearn import WindowRegression

model = WindowRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand Down Expand Up @@ -382,8 +375,6 @@ def ModelMonster(
)
return model
elif model == 'ComponentAnalysis':
from autots.models.sklearn import ComponentAnalysis

if parameters == {}:
model = ComponentAnalysis(
frequency=frequency,
Expand All @@ -408,8 +399,6 @@ def ModelMonster(
)
return model
elif model == 'DatepartRegression':
from autots.models.sklearn import DatepartRegression

model = DatepartRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand Down Expand Up @@ -624,8 +613,6 @@ def ModelMonster(
**parameters,
)
elif model == "PreprocessingRegression":
from autots.models.sklearn import PreprocessingRegression

return PreprocessingRegression(
frequency=frequency,
prediction_interval=prediction_interval,
Expand Down
49 changes: 21 additions & 28 deletions autots/models/sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,8 @@
try:
from sklearn import config_context
from sklearn.multioutput import MultiOutputRegressor, RegressorChain
from sklearn.linear_model import ElasticNet, MultiTaskElasticNet, LinearRegression, Ridge
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, ExtraTreeRegressor
except Exception:
pass
from autots.models.base import ModelObject, PredictionObject
Expand All @@ -36,6 +38,19 @@ def ppf(x):

# norm.ppf((1 + 0.95) / 2)

# for numba engine more is required, optional
try:
import numba # noqa
NUMBA_AVAILABLE = True
except ImportError:
NUMBA_AVAILABLE = False

# Check if the pandas version is 1.3 or greater and if numba is installed
if pd.__version__ >= '1.3' and NUMBA_AVAILABLE:
engine = 'numba'
else:
engine = None


def rolling_x_regressor(
df,
Expand Down Expand Up @@ -84,26 +99,26 @@ def rolling_x_regressor(
)
) # backfill should fill last values safely
if str(mean_rolling_periods).isdigit():
temp = local_df.rolling(int(mean_rolling_periods), min_periods=1).median()
temp = local_df.rolling(int(mean_rolling_periods), min_periods=1).median(engine=engine)
X.append(temp)
if str(macd_periods).isdigit():
# says mean, but median because it's been that way for ages
temp = local_df.rolling(int(macd_periods), min_periods=1).median() - temp
temp = local_df.rolling(int(macd_periods), min_periods=1).median(engine=engine) - temp
temp.columns = ['macd' for col in temp.columns]
X.append(temp)
if isinstance(mean_rolling_periods, list):
for mrp in mean_rolling_periods:
if isinstance(mrp, tuple):
if isinstance(mrp, (tuple, list)):
lag = mrp[0]
mean_roll = mrp[1]
temp = local_df.shift(lag).rolling(int(mean_roll), min_periods=1).mean().bfill()
temp = local_df.shift(lag).rolling(int(mean_roll), min_periods=1).mean(engine=engine).bfill()
temp.columns = [f'rollingmean_{lag}_{mean_roll}_' + str(col) for col in temp.columns]
else:
temp = local_df.rolling(int(mrp), min_periods=1).mean()
temp = local_df.rolling(int(mrp), min_periods=1).mean(engine=engine)
temp.columns = ['rollingmean_' + str(col) for col in temp.columns]
X.append(temp)
if str(macd_periods).isdigit():
temp = local_df.rolling(int(macd_periods), min_periods=1).mean() - temp
temp = local_df.rolling(int(macd_periods), min_periods=1).mean(engine=engine) - temp
temp.columns = ['macd' for col in temp.columns]
X.append(temp)
if str(std_rolling_periods).isdigit():
Expand Down Expand Up @@ -305,19 +320,13 @@ def retrieve_regressor(
model_param_dict = regression_model.get("model_params", {})
if model_class == 'ElasticNet':
if multioutput:
from sklearn.linear_model import MultiTaskElasticNet

regr = MultiTaskElasticNet(
alpha=1.0, random_state=random_seed, **model_param_dict
)
else:
from sklearn.linear_model import ElasticNet

regr = ElasticNet(alpha=1.0, random_state=random_seed, **model_param_dict)
return regr
elif model_class == 'DecisionTree':
from sklearn.tree import DecisionTreeRegressor

regr = DecisionTreeRegressor(random_state=random_seed, **model_param_dict)
return regr
elif model_class == 'MLP':
Expand Down Expand Up @@ -418,8 +427,6 @@ def retrieve_regressor(
random_state=random_seed,
)
elif regression_model["model_params"]['estimator'] == 'LinReg':
from sklearn.linear_model import LinearRegression

linreg = LinearRegression()
regr = AdaBoostRegressor(
estimator=linreg,
Expand All @@ -429,8 +436,6 @@ def retrieve_regressor(
random_state=random_seed,
)
elif regression_model["model_params"]['estimator'] == 'ElasticNet':
from sklearn.linear_model import LinearRegression

linreg = ElasticNet()
regr = AdaBoostRegressor(
estimator=linreg,
Expand All @@ -440,8 +445,6 @@ def retrieve_regressor(
random_state=random_seed,
)
elif regression_model["model_params"]['estimator'] == 'ExtraTree':
from sklearn.tree import ExtraTreeRegressor

linreg = ExtraTreeRegressor(max_depth=regression_model["model_params"].get("max_depth", 3))
regr = AdaBoostRegressor(
estimator=linreg,
Expand All @@ -451,8 +454,6 @@ def retrieve_regressor(
random_state=random_seed,
)
elif regression_model["model_params"].get("max_depth", None) is not None:
from sklearn.tree import DecisionTreeRegressor

linreg = DecisionTreeRegressor(max_depth=regression_model["model_params"].get("max_depth"))
regr = AdaBoostRegressor(
estimator=linreg,
Expand Down Expand Up @@ -494,12 +495,8 @@ def retrieve_regressor(
regr = LinearSVR(verbose=verbose_bool, **model_param_dict)
return regr
elif model_class == 'Ridge':
from sklearn.linear_model import Ridge

return Ridge(random_state=random_seed, **model_param_dict)
elif model_class == "FastRidge":
from sklearn.linear_model import Ridge

return Ridge(alpha=1e-9, solver="cholesky", fit_intercept=False, copy_X=False)
elif model_class == 'BayesianRidge':
from sklearn.linear_model import BayesianRidge
Expand Down Expand Up @@ -536,8 +533,6 @@ def retrieve_regressor(

return RANSACRegressor(random_state=random_seed, **model_param_dict)
elif model_class == "LinearRegression":
from sklearn.linear_model import LinearRegression

return LinearRegression(**model_param_dict)
elif model_class == "GaussianProcessRegressor":
from sklearn.gaussian_process import GaussianProcessRegressor
Expand Down Expand Up @@ -611,8 +606,6 @@ def retrieve_classifier(
n_jobs=n_jobs, random_state=random_seed, **model_param_dict
)
elif model_class == 'DecisionTree':
from sklearn.tree import DecisionTreeClassifier

return DecisionTreeClassifier(random_state=random_seed, **model_param_dict)
elif model_class in ['xgboost', 'XGBClassifier']:
import xgboost as xgb
Expand Down
12 changes: 6 additions & 6 deletions autots/tools/fir_filter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,8 +32,8 @@ def apply_fir_filter_to_timeseries(
"""

# Ensure the data has the correct shape: (observations, series)
if data.shape[0] < data.shape[1]:
data = data.T # Transpose if necessary to match (observations, series)
# if data.shape[0] < data.shape[1]:
# data = data.T # Transpose if necessary to match (observations, series)

# Normalize the cutoff frequency with respect to the Nyquist frequency
nyquist_frequency = 0.5 * sampling_frequency
Expand All @@ -59,8 +59,8 @@ def apply_fir_filter_time_domain(
This function has padding issues currently.
"""
# Ensure the data has the correct shape: (observations, series)
if data.shape[0] < data.shape[1]:
data = data.T # Transpose if necessary to match (observations, series)
# if data.shape[0] < data.shape[1]:
# data = data.T # Transpose if necessary to match (observations, series)

# Normalize the cutoff frequency with respect to the Nyquist frequency
nyquist_frequency = 0.5 * sampling_frequency
Expand Down Expand Up @@ -97,8 +97,8 @@ def fft_fir_filter_to_timeseries(
- filtered_data: The filtered version of the input data
"""
# Ensure the data has the correct shape: (observations, series)
if data.shape[0] < data.shape[1]:
data = data.T # Transpose if necessary to match (observations, series)
# if data.shape[0] < data.shape[1]:
# data = data.T # Transpose if necessary to match (observations, series)

# Normalize the cutoff frequency with respect to the Nyquist frequency
nyquist_frequency = 0.5 * sampling_frequency
Expand Down
26 changes: 15 additions & 11 deletions tests/test_transforms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from autots.datasets import (
load_daily, load_monthly, load_artificial, load_sine
)
from autots.tools.transform import ThetaTransformer
from autots.tools.transform import ThetaTransformer, FIRFilter

class TestTransforms(unittest.TestCase):

Expand All @@ -18,22 +18,26 @@ def test_theta(self):
'series2': np.random.randn(100).cumsum(),
}, index=dates)

# Initialize the transformer with custom theta values
theta_values = [0, 1, 2] # Example of custom theta values
theta_values = [0, 1, 2]
theta_transformer = ThetaTransformer(theta_values=theta_values)

params = theta_transformer.get_new_params()
self.assertTrue(params)

# Fit the transformer
theta_transformer.fit(data)

# Transform the data
transformed_data = theta_transformer.transform(data)

# Inverse transform to reconstruct the original data
reconstructed_data = theta_transformer.inverse_transform(transformed_data)

# Verify that the reconstructed data matches the original data
# Note: Due to numerical precision, a small tolerance is acceptable
self.assertTrue(np.allclose(data.values, reconstructed_data.values, atol=1e-8))

def test_firfilter(self):
df = load_daily(long=False)
transformer = FIRFilter()
transformed = transformer.fit_transform(df)
inverse = transformer.inverse_transform(transformed) # noqa

if False:
col = df.columns[0]
pd.concat([df[col], transformed[col].rename("transformed")], axis=1).plot()

self.assertCountEqual(transformed.index.tolist(), df.index.tolist())
self.assertCountEqual(transformed.columns.tolist(), df.columns.tolist())

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