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Merge pull request #57 from Techtonique/v0250
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update docs
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@thierrymoudiki
thierrymoudiki committed Sep 16, 2024
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248 changes: 3 additions & 245 deletions nnetsauce/mts/classical.py
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Expand Up @@ -39,281 +39,39 @@ class ClassicalMTS(Base):
model: type of model: str.
currently, 'VAR' or 'VECM'.
n_hidden_features: int.
number of nodes in the hidden layer.
activation_name: str.
activation function: 'relu', 'tanh', 'sigmoid', 'prelu' or 'elu'.
a: float.
hyperparameter for 'prelu' or 'elu' activation function.
nodes_sim: str.
type of simulation for the nodes: 'sobol', 'hammersley', 'halton',
'uniform'.
bias: boolean.
indicates if the hidden layer contains a bias term (True) or not
(False).
dropout: float.
regularization parameter; (random) percentage of nodes dropped out
of the training.
direct_link: boolean.
indicates if the original predictors are included (True) in model's fitting or not (False).
n_clusters: int.
number of clusters for 'kmeans' or 'gmm' clustering (could be 0: no clustering).
cluster_encode: bool.
defines how the variable containing clusters is treated (default is one-hot)
if `False`, then labels are used, without one-hot encoding.
type_clust: str.
type of clustering method: currently k-means ('kmeans') or Gaussian
Mixture Model ('gmm').
type_scaling: a tuple of 3 strings.
scaling methods for inputs, hidden layer, and clustering respectively
(and when relevant).
Currently available: standardization ('std') or MinMax scaling ('minmax').
lags: int.
number of lags used for each time series.
type_pi: str.
type of prediction interval; currently:
- "gaussian": simple, fast, but: assumes stationarity of Gaussian in-sample residuals and independence in the multivariate case
- "kde": based on Kernel Density Estimation of in-sample residuals
- "bootstrap": based on independent bootstrap of in-sample residuals
- "block-bootstrap": based on basic block bootstrap of in-sample residuals
- "scp-kde": Sequential split conformal prediction with Kernel Density Estimation of calibrated residuals
- "scp-bootstrap": Sequential split conformal prediction with independent bootstrap of calibrated residuals
- "scp-block-bootstrap": Sequential split conformal prediction with basic block bootstrap of calibrated residuals
- "scp2-kde": Sequential split conformal prediction with Kernel Density Estimation of standardized calibrated residuals
- "scp2-bootstrap": Sequential split conformal prediction with independent bootstrap of standardized calibrated residuals
- "scp2-block-bootstrap": Sequential split conformal prediction with basic block bootstrap of standardized calibrated residuals
- based on copulas of in-sample residuals: 'vine-tll', 'vine-bb1', 'vine-bb6', 'vine-bb7', 'vine-bb8', 'vine-clayton',
'vine-frank', 'vine-gaussian', 'vine-gumbel', 'vine-indep', 'vine-joe', 'vine-student'
- 'scp-vine-tll', 'scp-vine-bb1', 'scp-vine-bb6', 'scp-vine-bb7', 'scp-vine-bb8', 'scp-vine-clayton',
'scp-vine-frank', 'scp-vine-gaussian', 'scp-vine-gumbel', 'scp-vine-indep', 'scp-vine-joe', 'scp-vine-student'
- 'scp2-vine-tll', 'scp2-vine-bb1', 'scp2-vine-bb6', 'scp2-vine-bb7', 'scp2-vine-bb8', 'scp2-vine-clayton',
'scp2-vine-frank', 'scp2-vine-gaussian', 'scp2-vine-gumbel', 'scp2-vine-indep', 'scp2-vine-joe', 'scp2-vine-student'
block_size: int.
size of block for 'type_pi' in ("block-bootstrap", "scp-block-bootstrap", "scp2-block-bootstrap").
Default is round(3.15*(n_residuals^1/3))
replications: int.
number of replications (if needed, for predictive simulation). Default is 'None'.
kernel: str.
the kernel to use for residuals density estimation (used for predictive simulation). Currently, either 'gaussian' or 'tophat'.
agg: str.
either "mean" or "median" for simulation of bootstrap aggregating
seed: int.
reproducibility seed for nodes_sim=='uniform' or predictive simulation.
backend: str.
"cpu" or "gpu" or "tpu".
verbose: int.
0: not printing; 1: printing
show_progress: bool.
True: progress bar when fitting each series; False: no progress bar when fitting each series
Attributes:
fit_objs_: dict
objects adjusted to each individual time series
y_: {array-like}
FactorMTS responses (most recent observations first)
X_: {array-like}
FactorMTS lags
xreg_: {array-like}
external regressors
y_means_: dict
a dictionary of each series mean values
preds_: {array-like}
successive model predictions
preds_std_: {array-like}
standard deviation around the predictions for Bayesian base learners (`obj`)
gaussian_preds_std_: {array-like}
standard deviation around the predictions for `type_pi='gaussian'`
return_std_: boolean
return uncertainty or not (set in predict)
df_: data frame
the input data frame, in case a data.frame is provided to `fit`
n_obs_: int
number of time series observations (number of rows for multivariate)
level_: int
level of confidence for prediction intervals (default is 95)
residuals_: {array-like}
in-sample residuals (for `type_pi` not conformal prediction) or calibrated residuals
(for `type_pi` in conformal prediction)
residuals_sims_: tuple of {array-like}
simulations of in-sample residuals (for `type_pi` not conformal prediction) or
calibrated residuals (for `type_pi` in conformal prediction)
kde_: A scikit-learn object, see https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
residuals_std_dev_: residuals standard deviation
Examples:
Example 1:
```python
import nnetsauce as ns
import numpy as np
from sklearn import linear_model
np.random.seed(123)
M = np.random.rand(10, 3)
M[:,0] = 10*M[:,0]
M[:,2] = 25*M[:,2]
print(M)
# Adjust Bayesian Ridge
regr4 = linear_model.BayesianRidge()
obj_MTS = ns.FactorMTS(regr4, lags = 1, n_hidden_features=5)
obj_MTS.fit(M)
print(obj_MTS.predict())
# with credible intervals
print(obj_MTS.predict(return_std=True, level=80))
print(obj_MTS.predict(return_std=True, level=95))
```
Example 2:
```python
import nnetsauce as ns
import numpy as np
from sklearn import linear_model
dataset = {
'date' : ['2001-01-01', '2002-01-01', '2003-01-01', '2004-01-01', '2005-01-01'],
'series1' : [34, 30, 35.6, 33.3, 38.1],
'series2' : [4, 5.5, 5.6, 6.3, 5.1],
'series3' : [100, 100.5, 100.6, 100.2, 100.1]}
df = pd.DataFrame(dataset).set_index('date')
print(df)
# Adjust Bayesian Ridge
regr5 = linear_model.BayesianRidge()
obj_MTS = ns.FactorMTS(regr5, lags = 1, n_hidden_features=5)
obj_MTS.fit(df)
print(obj_MTS.predict())
# with credible intervals
print(obj_MTS.predict(return_std=True, level=80))
print(obj_MTS.predict(return_std=True, level=95))
```
See examples/classical_mts_timeseries.py
"""

# construct the object -----

def __init__(
self,
model="VAR",
n_hidden_features=5,
activation_name="relu",
a=0.01,
nodes_sim="sobol",
bias=True,
dropout=0,
direct_link=True,
n_clusters=2,
cluster_encode=True,
type_clust="kmeans",
type_scaling=("std", "std", "std"),
lags=1,
type_pi="kde",
block_size=None,
replications=None,
kernel="gaussian",
agg="mean",
seed=123,
backend="cpu",
verbose=0,
show_progress=True,
model="VAR"
):
assert int(lags) == lags, "parameter 'lags' should be an integer"

super().__init__(
n_hidden_features=n_hidden_features,
activation_name=activation_name,
a=a,
nodes_sim=nodes_sim,
bias=bias,
dropout=dropout,
direct_link=direct_link,
n_clusters=n_clusters,
cluster_encode=cluster_encode,
type_clust=type_clust,
type_scaling=type_scaling,
seed=seed,
backend=backend,
)

self.model = model
if self.model == "VAR":
self.obj = VAR
elif self.model == "VECM":
self.obj = VECM
self.n_series = None
self.lags = lags
self.type_pi = type_pi
self.block_size = block_size
self.replications = replications
self.kernel = kernel
self.agg = agg
self.verbose = verbose
self.show_progress = show_progress
self.series_names = None
self.input_dates = None
self.fit_objs_ = {}
self.y_ = None # FactorMTS responses (most recent observations first)
self.X_ = None # FactorMTS lags
self.xreg_ = None
self.y_means_ = {}
self.mean_ = None
self.upper_ = None
self.lower_ = None
self.output_dates_ = None
self.preds_std_ = []
self.gaussian_preds_std_ = None
self.alpha_ = None
self.return_std_ = None
self.df_ = None
self.residuals_ = []
self.abs_calib_residuals_ = None
self.calib_residuals_quantile_ = None
self.residuals_sims_ = None
self.kde_ = None
self.sims_ = None
self.residuals_std_dev_ = None
self.n_obs_ = None
self.level_ = None

def fit(self, X, **kwargs):
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -3,7 +3,7 @@
from codecs import open
from os import path

__version__ = '0.24.2'
__version__ = '0.24.3'

# get the dependencies and installs
here = path.abspath(path.dirname(__file__))
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