tods/tods/feature_analysis/AutoCorrelation.py

import os
import sklearn
import numpy
import typing
import time
from scipy import sparse
from numpy import ndarray
from collections import OrderedDict
from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple

import numpy as np
import pandas as pd
import logging, uuid
from scipy import sparse
from numpy import ndarray
from collections import OrderedDict
from common_primitives import dataframe_utils, utils

from d3m import utils
from d3m import container
from d3m.base import utils as base_utils
from d3m.exceptions import PrimitiveNotFittedError
from d3m.container import DataFrame as d3m_dataframe
from d3m.container.numpy import ndarray as d3m_ndarray
from d3m.primitive_interfaces import base, transformer
from d3m.metadata import base as metadata_base, hyperparams
from d3m.metadata import hyperparams, params, base as metadata_base
from d3m.primitive_interfaces.base import CallResult, DockerContainer
from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase

from statsmodels.tsa.stattools import acf


# import os.path


__all__ = ('AutoCorrelation',)


Inputs = d3m_dataframe
Outputs = d3m_dataframe

class PrimitiveCount:
    primitive_no = 0

class Params(params.Params):
    components_: Optional[ndarray]
    explained_variance_ratio_: Optional[ndarray]
    explained_variance_: Optional[ndarray]
    singular_values_: Optional[ndarray]
    input_column_names: Optional[Any]
    target_names_: Optional[Sequence[Any]]
    training_indices_: Optional[Sequence[int]]
    target_column_indices_: Optional[Sequence[int]]
    target_columns_metadata_: Optional[List[OrderedDict]]


class Hyperparams(hyperparams.Hyperparams):
	"""
	AutoCorrelation = hyperparams.Enumeration(
		values = ["acf", "pacf", "pacf_yw", "pacf_ols"],
		default = "acf",
		semantic_types=[],
		description='AutoCorrelation to use'
	)
	"""
	unbiased = hyperparams.UniformBool(
		default=False,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="If True, then denominators for autocovariance are n-k, otherwise n."
	)
	nlags = hyperparams.UniformInt(
		lower = 0,
		upper = 100,	#TODO: Define the correct the upper bound
		default=40,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Number of lags to return autocorrelation for."
	)
	qstat = hyperparams.UniformBool(
		default=False,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="If True, returns the Ljung-Box q statistic for each autocorrelationcoefficient."
	)
	fft = hyperparams.UniformBool(
		default=False,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="If True, computes the ACF via FFT."
	)
	alpha = hyperparams.Bounded[float](
		lower=0,
		upper=1,
		lower_inclusive=True,
		upper_inclusive=True,
		default = 0,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="""If a number is given, the confidence intervals for the given level are returned.
		For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett"s formula."""
	)
	missing = hyperparams.Enumeration[str](
		values=["none", "raise", "conservative", "drop"],
		default="none",
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="""Specifying how the NaNs are to be treated. "none" performs no checks. "raise" raises an exception if NaN values are found. 
		"drop" removes the missing observations and then estimates the autocovariances treating the non-missing as contiguous. 
		"conservative" computes the autocovariance using nan-ops so that nans are removed when computing the mean
		and cross-products that are used to estimate the autocovariance.
		When using "conservative", n is set to the number of non-missing observations."""
	)

	# Keep previous
	dataframe_resource = hyperparams.Hyperparameter[typing.Union[str, None]](
		default=None,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Resource ID of a DataFrame to extract if there are multiple tabular resources inside a Dataset and none is a dataset entry point.",
	)
	use_columns = hyperparams.Set(
		elements=hyperparams.Hyperparameter[int](-1),
		default=(2,),
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="A set of column indices to force primitive to operate on. If any specified column cannot be parsed, it is skipped.",
	)
	exclude_columns = hyperparams.Set(
		elements=hyperparams.Hyperparameter[int](-1),
		default=(0,1,3,),
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="A set of column indices to not operate on. Applicable only if \"use_columns\" is not provided.",
	)
	return_result = hyperparams.Enumeration(
		values=['append', 'replace', 'new'],
		default='append',
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Should parsed columns be appended, should they replace original columns, or should only parsed columns be returned? This hyperparam is ignored if use_semantic_types is set to false.",
	)
	use_semantic_types = hyperparams.UniformBool(
		default=False,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Controls whether semantic_types metadata will be used for filtering columns in input dataframe. Setting this to false makes the code ignore return_result and will produce only the output dataframe"
	)
	add_index_columns = hyperparams.UniformBool(
		default=False,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Also include primary index columns if input data has them. Applicable only if \"return_result\" is set to \"new\".",
	)
	error_on_no_input = hyperparams.UniformBool(
		default=True,
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
		description="Throw an exception if no input column is selected/provided. Defaults to true to behave like sklearn. To prevent pipelines from breaking set this to False.",
	)
	return_semantic_type = hyperparams.Enumeration[str](
		values=['https://metadata.datadrivendiscovery.org/types/Attribute',
			'https://metadata.datadrivendiscovery.org/types/ConstructedAttribute'],
		default='https://metadata.datadrivendiscovery.org/types/Attribute',
		description='Decides what semantic type to attach to generated attributes',
		semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter']
	)

class ACF:
	"""
	This is the class for autocorrelation function
	"""
	def __init__(self, unbiased=False, nlags=40, qstat=False, fft=None, alpha=None,missing="none"):
		self._unbiased = unbiased
		self._nlags = nlags
		self._qstat = qstat
		self._fft = fft
		self._alpha = alpha
		self._missing = missing
		self.primitiveNo = 0

	def produce(self, data):

		"""

		Args:
			data: dataframe column
		Returns:
			nparray

		"""

		transformed_columns=utils.pandas.DataFrame()
		for col in data.columns:
			output = acf(data[col], unbiased = self._unbiased, nlags = self._nlags, qstat = self._qstat, fft = self._fft, alpha = self._alpha, missing = self._missing)
			output = pd.DataFrame(output)
			transformed_columns=pd.concat([transformed_columns,output],axis=1)
		return transformed_columns



class AutoCorrelation(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperparams]):
	"""
	A primitive that performs autocorrelation on a DataFrame
	acf() function documentation: https://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.acf.html

	Parameters:
		-------
		x: array_like
			The time series data.
		
		unbiased: bool, default False
			If True, then denominators for autocovariance are n-k, otherwise n.
		
		nlags: int, default 40
			Number of lags to return autocorrelation for.
		
		qstat: bool, default False
			If True, returns the Ljung-Box q statistic for each autocorrelation coefficient. See q_stat for more information.
		
		fft: bool, default None
			If True, computes the ACF via FFT.
		
		alpha: scalar, default None
			If a number is given, the confidence intervals for the given level are returned. For instance if alpha=.05, 95 % confidence intervals are returned where the standard deviation is computed according to Bartlett”s formula.
		
		missing: str, default “none”
			A string in [“none”, “raise”, “conservative”, “drop”] specifying how the NaNs are to be treated. “none” performs no checks. “raise” raises an exception if NaN values are found. “drop” removes the missing observations and then estimates the autocovariances treating the non-missing as contiguous. “conservative” computes the autocovariance using nan-ops so that nans are removed when computing the mean and cross-products that are used to estimate the autocovariance. When using “conservative”, n is set to the number of non-missing observations.
		-------
	"""
	
	metadata = metadata_base.PrimitiveMetadata({
		'__author__': "DATA Lab @Texas A&M University",
		'name': "AutoCorrelation of values",
		'python_path': 'd3m.primitives.tods.feature_analysis.auto_correlation',
		'source': {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu',
				   'uris': ['https://gitlab.com/lhenry15/tods/-/blob/Yile/anomaly-primitives/anomaly_primitives/AutoCorrelation.py']},
		'algorithm_types': [metadata_base.PrimitiveAlgorithmType.AUTOCORRELATION,], 
		'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION,
		'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'AutocorrelationPrimitive')),
		'hyperparams_to_tune': ['unbiased', 'nlags', 'qstat', 'fft', 'alpha', 'missing'],
		'version': '0.0.2',		
		})

	def __init__(self, *,
		 hyperparams: Hyperparams, #
		 random_seed: int = 0,
		 docker_containers: Dict[str, DockerContainer] = None) -> None:
		super().__init__(hyperparams=hyperparams, random_seed=random_seed, docker_containers=docker_containers)


		self._clf = ACF(unbiased = hyperparams['unbiased'],
						nlags = hyperparams['nlags'],
						qstat = hyperparams['qstat'],
						fft = hyperparams['fft'],
						alpha = hyperparams['alpha'],
						missing = hyperparams['missing']
	 				)

		self.primitiveNo = PrimitiveCount.primitive_no
		PrimitiveCount.primitive_no+=1


	def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]:	
		"""
		Process the testing data.
		Args:
			inputs: Container DataFrame.

		Returns:
			Container DataFrame after AutoCorrelation.
		"""

		# Get cols to fit.
		self._fitted = False
		self._training_inputs, self._training_indices = self._get_columns_to_fit(inputs, self.hyperparams)
		self._input_column_names = self._training_inputs.columns

		print("training_indices_ ", self._training_indices)
		if len(self._training_indices) > 0:
			self._fitted = True
		else:	# pragma: no cover
			if self.hyperparams['error_on_no_input']:
				raise RuntimeError("No input columns were selected")
			self.logger.warn("No input columns were selected")

		if not self._fitted:	# pragma: no cover
			raise PrimitiveNotFittedError("Primitive not fitted.")
		
		sk_inputs = inputs
		if self.hyperparams['use_semantic_types']:	# pragma: no cover
			sk_inputs = inputs.iloc[:, self._training_indices]
		output_columns = []
		if len(self._training_indices) > 0:
			print("sk_inputs ", sk_inputs)
			sk_output = self._clf.produce(sk_inputs)
			if sparse.issparse(sk_output):	# pragma: no cover
				sk_output = sk_output.toarray()
			outputs = self._wrap_predictions(inputs, sk_output)
			
			if len(outputs.columns) == len(self._input_column_names):
				outputs.columns = self._input_column_names
			output_columns = [outputs]

		else:	# pragma: no cover
			if self.hyperparams['error_on_no_input']:
				raise RuntimeError("No input columns were selected")
			self.logger.warn("No input columns were selected")

		outputs = base_utils.combine_columns(return_result=self.hyperparams['return_result'],
							   add_index_columns=self.hyperparams['add_index_columns'],
							   inputs=inputs, column_indices=self._training_indices,
							   columns_list=output_columns)

		return CallResult(outputs)





	def _update_metadata(self, outputs):	# pragma: no cover
		outputs.metadata = outputs.metadata.generate(outputs)
 
	@classmethod
	def _get_columns_to_fit(cls, inputs: Inputs, hyperparams: Hyperparams):	# pragma: no cover
		"""
			    Select columns to fit.
			    Args:
			inputs: Container DataFrame
			hyperparams: d3m.metadata.hyperparams.Hyperparams
			    Returns:
			list
		"""

		if not hyperparams['use_semantic_types']:
			return inputs, list(range(len(inputs.columns)))

		inputs_metadata = inputs.metadata

		def can_produce_column(column_index: int) -> bool:

			return cls._can_produce_column(inputs_metadata, column_index, hyperparams)

		columns_to_produce, columns_not_to_produce = base_utils.get_columns_to_use(inputs_metadata,
					   use_columns=hyperparams['use_columns'],
					   exclude_columns=hyperparams['exclude_columns'],
					   can_use_column=can_produce_column)

		"""
		Encountered error: when hyperparams['use_columns'] = (2,3) and hyperparams['exclude_columns'] is (1,2)
		columns_to_produce is still [2]
		"""
		return inputs.iloc[:, columns_to_produce], columns_to_produce
		

	@classmethod
	def _can_produce_column(cls, inputs_metadata: metadata_base.DataMetadata, column_index: int, hyperparams: Hyperparams) -> bool:	# pragma: no cover
		"""
			    Output whether a column can be processed.

			    Args:
			inputs_metadata: d3m.metadata.base.DataMetadata
			column_index: int
			    Returns:
			bool
		"""

		column_metadata = inputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index))

		accepted_structural_types = (int, float, np.integer, np.float64) #changed numpy to np
		accepted_semantic_types = set()
		accepted_semantic_types.add("https://metadata.datadrivendiscovery.org/types/Attribute")
		print("accepted_semantic_types ", accepted_semantic_types)
		print("column_metadata['structural_type'] ",column_metadata['structural_type'])
		if not issubclass(column_metadata['structural_type'], accepted_structural_types):
			return False

		semantic_types = set(column_metadata.get('semantic_types', []))
		print("semantic_types ", semantic_types)
		if len(semantic_types) == 0:
			cls.logger.warning("No semantic types found in column metadata")
			return False

		# Making sure all accepted_semantic_types are available in semantic_types
		if len(accepted_semantic_types - semantic_types) == 0:
			return True

		return False

	def _wrap_predictions(self, inputs: Inputs, predictions: ndarray) -> Outputs:

		"""
		Wrap predictions into dataframe

		Args:
			inputs: Container Dataframe
			predictions: array-like data (n_samples, n_features)

		Returns:
			Dataframe

		"""

		outputs = d3m_dataframe(predictions, generate_metadata=True)
		target_columns_metadata = self._add_target_columns_metadata(outputs.metadata, self.hyperparams, self.primitiveNo)
		outputs.metadata = self._update_predictions_metadata(inputs.metadata, outputs, target_columns_metadata)
		return outputs




	@classmethod
	def _update_predictions_metadata(cls, inputs_metadata: metadata_base.DataMetadata, outputs: Optional[Outputs],
									target_columns_metadata: List[OrderedDict]) -> metadata_base.DataMetadata:
		"""
			    Updata metadata for selected columns.

			    Args:
			inputs_metadata: metadata_base.DataMetadata
			outputs: Container Dataframe
			target_columns_metadata: list

			    Returns:
			d3m.metadata.base.DataMetadata
		"""

		outputs_metadata = metadata_base.DataMetadata().generate(value=outputs)

		for column_index, column_metadata in enumerate(target_columns_metadata):
			column_metadata.pop("structural_type", None)
			outputs_metadata = outputs_metadata.update_column(column_index, column_metadata)

		return outputs_metadata


	@classmethod
	def _add_target_columns_metadata(cls, outputs_metadata: metadata_base.DataMetadata, hyperparams, primitiveNo):
		"""
		Add target columns metadata
		Args:
			outputs_metadata: metadata.base.DataMetadata
			hyperparams: d3m.metadata.hyperparams.Hyperparams

		Returns:
			List[OrderedDict]
		"""
		outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length']
		target_columns_metadata: List[OrderedDict] = []
		for column_index in range(outputs_length):
			column_name = "{0}{1}_{2}".format(cls.metadata.query()['name'], primitiveNo, column_index)
			column_metadata = OrderedDict()
			semantic_types = set()
			semantic_types.add(hyperparams["return_semantic_type"])
			column_metadata['semantic_types'] = list(semantic_types)

			column_metadata["name"] = str(column_name)
			target_columns_metadata.append(column_metadata)
		return target_columns_metadata