def fit(self):
"""
DESC: Fit (estimate) the parameters of the model
RETURNS: TYPE: list of shape (n_samples,), returns the fitted values
of the model
"""
self.reset_metadata()
if len(self.order) != 3:
raise ValueError("`order` argument must be an iterable with " + \
"three elements.")
if self.ar_lag < 1:
raise ValueError("Terms in the AR order cannot be less than 1.")
if self.diff_order < 0:
raise ValueError("Cannot specify negative differencing.")
if self.ma_lag < 0:
raise ValueError("Terms in the MA order cannot be negative.")
if self.seasonal is None:
self.seasonal = 0
elif self.seasonal < 0:
raise ValueError("The seasonal differencing interval cannot " + \
"be negative, given: " + str(self.seasonal))
if self.auto_arima is True and self.ma_lag <= 0:
raise ValueError("Currently, auto_arima cannot start with a " + \
"moving average component having value less " + \
"than 1!")
if self.solver == 'sag':
self.solver = 'sgd'
elif self.solver not in ['lapack', 'lbfgs', 'scalapack']:
raise ValueError("Unknown solver: " + self.solver + " for time " + \
"series analysis!")
if isinstance(self.endog, FrovedisDvector):
self.__mdtype = self.endog.get_dtype()
inp_data = self.endog.to_numpy_array()
shape = np.shape(inp_data)
if np.shape(inp_data)[0] < (self.ar_lag + self.diff_order + \
self.ma_lag + self.seasonal + 2):
raise ValueError("Number of samples in input is too less " + \
"for time series analysis!")
self._endog_len = shape[0]
elif isinstance(self.endog, (FrovedisCRSMatrix, FrovedisDenseMatrix,
FrovedisRowmajorMatrix, FrovedisColmajorMatrix)):
raise TypeError("endog can only be FrovedisDvector, " + \
"not {}".format(self.endog))
else:
shape = np.shape(self.endog)
if len(shape) == 1 or (len(shape) == 2 and shape[1] == 1):
if np.shape(self.endog)[0] < (self.ar_lag + self.diff_order + \
self.ma_lag + self.seasonal + 2):
raise ValueError("Number of samples in input is too " + \
"less for time series analysis!")
self.endog = np.ravel(self.endog)
self._endog_len = shape[0]
self.endog = FrovedisDvector().as_dvec(self.endog)
self.__mdtype = self.endog.get_dtype()
else:
raise ValueError("Frovedis ARIMA models require univariate " + \
"`endog`. Got shape {0}".format(shape))
self.__mkind = M_KIND.ARM
(host, port) = FrovedisServer.getServerInstance()
rpclib.arima_fit(host, port, self.endog.get(), self.ar_lag, \
self.diff_order, self.ma_lag, self.seasonal, \
self.auto_arima, str_encode(self.solver), \
self.verbose, self.__mid, self.__mdtype)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
self.isfitted = True
return self
class ARIMA(BaseEstimator):
"""
DESC: AR-Auto Regressive(p) I-Integrated(d) MA-Moving Average(q) model
A time series model that is used to forecast data based on the dataset of
past to predict/forecast the future
HYPERPARAMS: endog -> TYPE: array-like, contains the time series data
exog -> DEFAULT: None, (Unused)
order -> TYPE: Tuple, DEFAULT: (1,0,0), (p,d,q) order of the
model for the autoregressive, differences, and
moving average components where p,d,q are integers
seasonal_order -> DEFAULT: (0,0,0,0), (Unused)
trend -> DEFAULT: None, (Unused)
enforce_stationarity -> DEFAULT: True, (Unused)
enforce_invertibility -> DEFAULT: True, (Unused)
concentrate_scale -> DEFAULT: False, (Unused)
trend_offset -> DEFAULT: 1, (Unused)
dates -> DEFAULT: None, (Unused)
freq -> DEFAULT: None, (Unused)
missing -> DEFAULT: 'none', (Unused)
validate_specification -> DEFAULT: True, (Unused)
seasonal -> TYPE: int, DEFAULT: None, it specifies the
interval of seasonal differencing i.e if the data
has some seasonality, then it can also handle it.
This is optional.
auto_arima -> TYPE: bool, DEFAULT: False, it specifies whether
to use auto (brute) ARIMA. If true it treats
the p and q parameter as the highest limit for
its iteration and auto fit lags with best RMSE.
This is optional
solver -> TYPE: str, DEFAULT: 'lapack', it uses 'lapack',
'lbfgs', 'sag', 'scalapack' as solver to perform
linear regression
verbose -> TYPE: int, DEFAULT: 0, it specifies the log level
to use. It uses 0 (for INFO mode), 1 (for DEBUG
mode) or 2 (for TRACE mode) for getting training
time logs from frovedis server
"""
def __init__(self, endog, exog=None, order=(1, 0, 0),
seasonal_order=(0, 0, 0, 0), trend=None,
enforce_stationarity=True, enforce_invertibility=True,
concentrate_scale=False, trend_offset=1, dates=None,freq=None,
missing='none', validate_specification=True, seasonal=None,
auto_arima=False, solver='lapack', verbose=0):
self.endog = endog
self.exog = exog
self.order = order
self.ar_lag = 1 if order[0] is None else order[0]
self.diff_order = 0 if order[1] is None else order[1]
self.ma_lag = 0 if order[2] is None else order[2]
self.seasonal_order = seasonal_order
self.trend = trend
self.enforce_stationarity = enforce_stationarity
self.enforce_invertibility = enforce_invertibility
self.concentrate_scale = concentrate_scale
self.trend_offset = trend_offset
self.dates = dates
self.freq = freq
self.missing = missing
self.validate_specification = validate_specification
self.seasonal = seasonal
self.auto_arima = auto_arima
self.solver = solver
self.verbose = verbose
# extra
self.__mid = ModelID.get()
self.__mdtype = None
self.__mkind = None
self._fittedvalues = None
self.isfitted = False
self._endog_len = None
@set_association
def fit(self):
"""
DESC: Fit (estimate) the parameters of the model
RETURNS: TYPE: list of shape (n_samples,), returns the fitted values
of the model
"""
self.reset_metadata()
if len(self.order) != 3:
raise ValueError("`order` argument must be an iterable with " + \
"three elements.")
if self.ar_lag < 1:
raise ValueError("Terms in the AR order cannot be less than 1.")
if self.diff_order < 0:
raise ValueError("Cannot specify negative differencing.")
if self.ma_lag < 0:
raise ValueError("Terms in the MA order cannot be negative.")
if self.seasonal is None:
self.seasonal = 0
elif self.seasonal < 0:
raise ValueError("The seasonal differencing interval cannot " + \
"be negative, given: " + str(self.seasonal))
if self.auto_arima is True and self.ma_lag <= 0:
raise ValueError("Currently, auto_arima cannot start with a " + \
"moving average component having value less " + \
"than 1!")
if self.solver == 'sag':
self.solver = 'sgd'
elif self.solver not in ['lapack', 'lbfgs', 'scalapack']:
raise ValueError("Unknown solver: " + self.solver + " for time " + \
"series analysis!")
if isinstance(self.endog, FrovedisDvector):
self.__mdtype = self.endog.get_dtype()
inp_data = self.endog.to_numpy_array()
shape = np.shape(inp_data)
if np.shape(inp_data)[0] < (self.ar_lag + self.diff_order + \
self.ma_lag + self.seasonal + 2):
raise ValueError("Number of samples in input is too less " + \
"for time series analysis!")
self._endog_len = shape[0]
elif isinstance(self.endog, (FrovedisCRSMatrix, FrovedisDenseMatrix,
FrovedisRowmajorMatrix, FrovedisColmajorMatrix)):
raise TypeError("endog can only be FrovedisDvector, " + \
"not {}".format(self.endog))
else:
shape = np.shape(self.endog)
if len(shape) == 1 or (len(shape) == 2 and shape[1] == 1):
if np.shape(self.endog)[0] < (self.ar_lag + self.diff_order + \
self.ma_lag + self.seasonal + 2):
raise ValueError("Number of samples in input is too " + \
"less for time series analysis!")
self.endog = np.ravel(self.endog)
self._endog_len = shape[0]
self.endog = FrovedisDvector().as_dvec(self.endog)
self.__mdtype = self.endog.get_dtype()
else:
raise ValueError("Frovedis ARIMA models require univariate " + \
"`endog`. Got shape {0}".format(shape))
self.__mkind = M_KIND.ARM
(host, port) = FrovedisServer.getServerInstance()
rpclib.arima_fit(host, port, self.endog.get(), self.ar_lag, \
self.diff_order, self.ma_lag, self.seasonal, \
self.auto_arima, str_encode(self.solver), \
self.verbose, self.__mid, self.__mdtype)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
self.isfitted = True
return self
@check_association
def predict(self, start=None, end=None, dynamic=False, **kwargs):
"""
DESC: Perform in-sample prediction and out-of-sample forecasting
PARAMS: start -> TYPE: int, DEFAULT: None, it specifies the staring
index after which the values are to be predicted
stop -> TYPE: int, DEFAULT: None, it specifies the index till
which the values are to be predicted
dynamic -> DEFAULT: False, (Unused)
**kwargs -> (Unused)
NOTE: In case start or stop are negative then predicted values are
returned from fitted values as long as indexes are accessible(in
range). This is in sync with statsmodel behaviour.
RETURNS: TYPE: ndarray of shape (n_samples,), it returns the predicted
values
"""
if start is None:
start = 0
elif start < 0:
if self._endog_len >= abs(start):
start = self._endog_len + start
else:
raise KeyError("The `start` argument could not be matched " + \
"to a location related to the index of " + \
"the data.")
if end is None:
end = self._endog_len - 1
elif end < 0:
if self._endog_len >= abs(end):
end = self._endog_len + end
else:
raise KeyError("The `end` argument could not be matched to " + \
"a location related to the index of the data.")
if end < start:
raise ValueError("Prediction must have `end` after `start`!")
if dynamic:
raise ValueError("Currently, ARIMA.predict() does not support " + \
"dynamic = True!")
(host, port) = FrovedisServer.getServerInstance()
arima_pred = rpclib.arima_predict(host, port, start, end, \
self.__mid, self.__mdtype)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
return np.asarray(arima_pred, dtype = np.float64)
@check_association
def forecast(self, steps=1, exog=None, alpha=0.05):
"""
DESC: Perform out-of-sample forecasting
PARAMS: steps -> TYPE: int, DEFAULT: 1, it specifies the number of
out-of-sample values to be predicted
exog -> DEFAULT: None, (Unused)
alpha -> DEFAULT: 0.05, (Unused)
RETURNS: TYPE: ndarray of shape (n_samples,), returns out of sample
forecasted values
"""
if exog is not None:
raise ValueError("Currently, exog is not supported by forecast()!")
if alpha != 0.05:
raise ValueError("Currently, alpha is not supported by forecast()!")
if steps is None:
steps = 1
elif steps <= 0:
raise ValueError("Prediction must have `end` after `start`!")
(host, port) = FrovedisServer.getServerInstance()
fcast = rpclib.arima_forecast(host, port, steps, self.__mid, \
self.__mdtype)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
return np.asarray(fcast, dtype = np.float64)
@property
@check_association
def fittedvalues(self):
"""
DESC: fittedvalues getter
RETURNS: TYPE: ndarray of shape (n_samples,), returns the fitted
values of the model
"""
if self._fittedvalues is None:
(host, port) = FrovedisServer.getServerInstance()
ret = rpclib.get_fitted_vector(host, port, self.__mid, \
self.__mkind, self.__mdtype)
excpt = rpclib.check_server_exception()
if excpt["status"]:
raise RuntimeError(excpt["info"])
self._fittedvalues = np.asarray(ret, dtype=np.float64)
return self._fittedvalues
@fittedvalues.setter
def fittedvalues(self, val):
"""
DESC: fittedvalues setter
"""
raise AttributeError(\
"attribute 'fittedvalues' of ARIMA object is not writable")
def reset_metadata(self):
"""
DESC: resets after-fit populated attributes to None
"""
self._fittedvalues = None
self.isfitted = False
def release(self):
"""
DESC: resets after-fit populated attributes to None
along with relasing server side memory
"""
self.__release_server_heap()
self.reset_metadata()
@do_if_active_association
def __release_server_heap(self):
"""
DESC: to release model pointer from server heap
"""
GLM.release(self.__mid, self.__mkind, self.__mdtype)
def __del__(self):
"""
DESC: destructs the python object
"""
self.release()
def is_fitted(self):
"""
DESC: function to confirm if the ARIMA model is already fitted
"""
return self.isfitted
import os
import numpy as np
from frovedis.exrpc.server import FrovedisServer
from frovedis.matrix.dvector import FrovedisDvector
from frovedis.matrix.dense import FrovedisRowmajorMatrix
FrovedisServer.initialize("mpirun -np 2 {}".format(
os.environ['FROVEDIS_SERVER']))
dv = FrovedisDvector([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float64)
dv.debug_print()
FrovedisServer.shut_down()
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.matrix.dvector import FrovedisDvector
from frovedis.mllib.svm import LinearSVC
import numpy as np
import sys
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")')
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/libSVMFile.txt")
lbl = FrovedisDvector([1,0,1,1,1,0,1,1],dtype=np.float64)
# fitting input matrix and label on linear svm object
svm = LinearSVC(solver='lbfgs',verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on lbfgs svm regression model: ")
print(svm.predict(mat))
# fitting input matrix and label on linear svm object
svm = LinearSVC(solver='sag',verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on sgd svm regression model: ")
print(svm.predict(mat))
from frovedis.matrix.crs import FrovedisCRSMatrix
from frovedis.matrix.dvector import FrovedisDvector
from frovedis.mllib.linear_model import *
import numpy as np
import sys
# initializing the Frovedis server
argvs = sys.argv
argc = len(argvs)
if (argc < 2):
print ('Please give frovedis_server calling command as the first argument \n(e.g. "mpirun -np 2 -x /opt/nec/nosupport/frovedis/ve/bin/frovedis_server")')
quit()
FrovedisServer.initialize(argvs[1])
mat = FrovedisCRSMatrix(dtype=np.float64).load("./input/libSVMFile.txt")
lbl = FrovedisDvector([1.1,0.2,1.3,1.4,1.5,0.6,1.7,1.8],dtype=np.float64)
# fitting input matrix and label on linear regression object
lr = LinearRegression(solver='lbfgs', verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on lbfgs linear regression model: ")
print(lr.predict(mat))
# fitting input matrix and label on linear regression object
lr = LinearRegression(solver='sag', verbose=0).fit(mat,lbl)
# predicting on loaded model
print("predicting on sag linear regression model: ")
print(lr.predict(mat))