tods/tods/detection_algorithm/Ensemble.py

from typing import Any, Callable, List, Dict, Union, Optional, Sequence, Tuple
from numpy import ndarray
from collections import OrderedDict
from scipy import sparse
import os
import sklearn
import numpy
import typing
import pandas as pd
# Custom import commands if any
from sklearn.preprocessing.data import Normalizer
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
import uuid


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

import os
from typing import Any,Optional,List

from d3m import container, utils as d3m_utils
from d3m.metadata import base as metadata_base
from d3m.metadata import hyperparams,params
from d3m.primitive_interfaces import base, transformer

Inputs = d3m_dataframe
Outputs = d3m_dataframe


class Params(params.Params):
    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):
    # Added by Mia
    endog = hyperparams.Bounded[int](
        lower = 2,
        upper = None,
        default = 3,
        description='Array like time series.',
        semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
    )

    threshold = hyperparams.Bounded[float](
        lower = 0,
        upper = 1,
        default = 0.5,
        semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
    )

    # keep previous
    norm = hyperparams.Enumeration[str](
        default='l2',
        values=['l1', 'l2', 'max'],
        description='The norm to use to normalize each non zero sample.',
        semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
    )
    
    use_columns = hyperparams.Set(
        elements=hyperparams.Hyperparameter[int](-1),
        default=(),
        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=(),
        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='new',
        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 EnsemblePrimitive(UnsupervisedLearnerPrimitiveBase[Inputs, Outputs, Params, Hyperparams]):
    """
   Ensemble method 
    `Calculate the Maximum/Minimum/Average and  Majority Voting for the detection algorithm based on the threshold set for the score`_
    
    """
    
    metadata = metadata_base.PrimitiveMetadata({
         "__author__": "DATA Lab at Texas A&M University",
         "name": "Ensemble",
         "python_path": "d3m.primitives.tods.detection_algorithm.Ensemble",
         "source": {
             'name': 'DATA Lab at Texas A&M University', 
             'contact': 'mailto:khlai037@tamu.edu', 
         },
         "version": "0.0.1",
         "hyperparams_to_tune": ['use_columns'],
         "algorithm_types": [
             metadata_base.PrimitiveAlgorithmType.TODS_PRIMITIVE, 
         ],
         "primitive_family": metadata_base.PrimitiveFamily.ANOMALY_DETECTION,
	 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'EnsemblePrimitive')),
     })

    def __init__(self, *, # pragma: no cover
                 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)
        
        # False
        self._clf = Normalizer(
              norm=self.hyperparams['norm'],
        )
        
        self._inputs = None
        self._outputs = None
        self._training_inputs = None
        self._training_outputs = None
        self._target_names = None
        self._training_indices = None
        self._target_column_indices = None
        self._target_columns_metadata: List[OrderedDict] = None
        self._input_column_names = None
        self._fitted = False
        
        
    def set_training_data(self, *, inputs: Inputs) -> None: # pragma: no cover
        self._inputs = inputs
        self._fitted = False
        
    def fit(self, *, timeout: float = None, iterations: int = None)-> CallResult[None]: # pragma: no cover
        if self._fitted:
            return CallResult(None)

        self._training_inputs, self._training_indices = self._get_columns_to_fit(self._inputs, self.hyperparams)
        self._input_column_names = self._training_inputs.columns

        if self._training_inputs is None:
            return CallResult(None)

        if len(self._training_indices) > 0:
            self._clf.fit(self._training_inputs)
            self._fitted = True
        else:
            if self.hyperparams['error_on_no_input']:
                raise RuntimeError("No input columns were selected")
            self.logger.warn("No input columns were selected")
        return CallResult(None)
        
    def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: # pragma: no cover

        outputs = inputs
        outputs.columns = ['timestamp','value','system_id','scores']
#        print(outputs)
#        print('max_score')
#        ensemble_max = outputs.groupby('system_id')[outputs.columns[-1]].max()

#        print(ensemble_max)
#
#        print('min_score')
#        ensemble_min = outputs.groupby('system_id')[outputs.columns[-1]].min()
#        print(ensemble_min)
#   
#        print('mean_score')     
#        outputs_mean = outputs.groupby('system_id')[outputs.columns[3]].mean()
#        print(outputs_mean) 

        outputs['results'] = numpy.where(outputs['scores']>0.05, 1, 0)
        print(outputs)

        outputs_xy = outputs.groupby('system_id')['results'].sum().reset_index()
        print("*****majority_sum_xy*****")
        print(outputs_xy)

        outputs_sum_x = outputs.groupby(['timestamp','system_id'])['results'].sum()
#        outputs_sum_x = outputs.groupby(['system_id','timestamp']).size().reset_index().groupby(['timestamp'])['results'].sum() 

        outputs_sum_y = outputs.groupby(['system_id','value'])['results'].sum()

        print('*****majority_max_x*****')
        print(outputs_sum_x)
        print('*****majority_max_y*****')
        print(outputs_sum_y)

        return base.CallResult(outputs) 

    def _update_metadata(self, outputs): # pragma: no cover
        outputs.metadata = outputs.metadata.generate(outputs,) 

    def get_params(self) -> Params: # pragma: no cover
        if not self._fitted:
            return Params(
                input_column_names=self._input_column_names,
                training_indices_=self._training_indices,
                target_names_=self._target_names,
                target_column_indices_=self._target_column_indices,
                target_columns_metadata_=self._target_columns_metadata
            )

        return Params(
            input_column_names=self._input_column_names,
            training_indices_=self._training_indices,
            target_names_=self._target_names,
            target_column_indices_=self._target_column_indices,
            target_columns_metadata_=self._target_columns_metadata
        )

    def set_params(self, *, params: Params) -> None: # pragma: no cover
        self._input_column_names = params['input_column_names']
        self._training_indices = params['training_indices_']
        self._target_names = params['target_names_']
        self._target_column_indices = params['target_column_indices_']
        self._target_columns_metadata = params['target_columns_metadata_']
        self._fitted = True



    
    
    @classmethod
    def _get_columns_to_fit(cls, inputs: Inputs, hyperparams: Hyperparams): # pragma: no cover
        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)
        return inputs.iloc[:, columns_to_produce], columns_to_produce
        # return columns_to_produce

    @classmethod
    def _can_produce_column(cls, inputs_metadata: metadata_base.DataMetadata, column_index: int, hyperparams: Hyperparams) -> bool: # pragma: no cover
        column_metadata = inputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index))

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

        semantic_types = set(column_metadata.get('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
    

    @classmethod
    def _get_target_columns_metadata(cls, outputs_metadata: metadata_base.DataMetadata, hyperparams) -> List[OrderedDict]: # pragma: no cover
        outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length']

        target_columns_metadata: List[OrderedDict] = []
        for column_index in range(outputs_length):
            column_metadata = OrderedDict(outputs_metadata.query_column(column_index))

            # Update semantic types and prepare it for predicted targets.
            semantic_types = set(column_metadata.get('semantic_types', []))
            semantic_types_to_remove = set([])
            add_semantic_types = []
            add_semantic_types.add(hyperparams["return_semantic_type"])
            semantic_types = semantic_types - semantic_types_to_remove
            semantic_types = semantic_types.union(add_semantic_types)
            column_metadata['semantic_types'] = list(semantic_types)

            target_columns_metadata.append(column_metadata)

        return target_columns_metadata
    
    @classmethod
    def _update_predictions_metadata(cls, inputs_metadata: metadata_base.DataMetadata, outputs: Optional[Outputs], # pragma: no cover
                                     target_columns_metadata: List[OrderedDict]) -> 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

    def _wrap_predictions(self, inputs: Inputs, predictions: ndarray) -> Outputs: # pragma: no cover
        outputs = d3m_dataframe(predictions, generate_metadata=True)
        target_columns_metadata = self._copy_inputs_metadata(inputs.metadata, self._training_indices, outputs.metadata, self.hyperparams)
        outputs.metadata = self._update_predictions_metadata(inputs.metadata, outputs, target_columns_metadata)
        return outputs


    @classmethod
    def _copy_inputs_metadata(cls, inputs_metadata: metadata_base.DataMetadata, input_indices: List[int], # pragma: no cover
                                        outputs_metadata: metadata_base.DataMetadata, hyperparams):
        outputs_length = outputs_metadata.query((metadata_base.ALL_ELEMENTS,))['dimension']['length']
        target_columns_metadata: List[OrderedDict] = []
        for column_index in input_indices:
            column_name = inputs_metadata.query((metadata_base.ALL_ELEMENTS, column_index)).get("name")
            if column_name is None:
                column_name = "output_{}".format(column_index)

            column_metadata = OrderedDict(inputs_metadata.query_column(column_index))
            semantic_types = set(column_metadata.get('semantic_types', []))
            semantic_types_to_remove = set([])
            add_semantic_types = set()
            add_semantic_types.add(hyperparams["return_semantic_type"])
            semantic_types = semantic_types - semantic_types_to_remove
            semantic_types = semantic_types.union(add_semantic_types)
            column_metadata['semantic_types'] = list(semantic_types)

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

        #  If outputs has more columns than index, add Attribute Type to all remaining
        if outputs_length > len(input_indices):
            for column_index in range(len(input_indices), outputs_length):
                column_metadata = OrderedDict()
                semantic_types = set()
                semantic_types.add(hyperparams["return_semantic_type"])
                column_name = "output_{}".format(column_index)
                column_metadata["semantic_types"] = list(semantic_types)
                column_metadata["name"] = str(column_name)
                target_columns_metadata.append(column_metadata)

        return target_columns_metadata


EnsemblePrimitive.__doc__ = Normalizer.__doc__