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 import stumpy __all__ = ('MatrixProfile',) Inputs = container.DataFrame Outputs = container.DataFrame class PrimitiveCount: primitive_no = 0 class Hyperparams(hyperparams.Hyperparams): window_size = hyperparams.UniformInt( lower = 0, upper = 100, #TODO: Define the correct the upper bound default=50, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="window size to calculate" ) # 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='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 MP: """ This is the class for matrix profile function """ def __init__(self, window_size): self._window_size = window_size return def produce(self, data): """ Args: data: dataframe column Returns: nparray """ transformed_columns=utils.pandas.DataFrame() #transformed_columns=d3m_dataframe for col in data.columns: output = stumpy.stump(data[col], m = self._window_size) output = pd.DataFrame(output) #print("output", output) transformed_columns=pd.concat([transformed_columns,output],axis=1) #transformed_columns[col]=output #print(transformed_columns) return transformed_columns class MatrixProfile(transformer.TransformerPrimitiveBase[Inputs, Outputs, Hyperparams]): """ A primitive that performs matrix profile on a DataFrame using Stumpy package Stumpy documentation: https://stumpy.readthedocs.io/en/latest/index.html Parameters ---------- T_A : ndarray The time series or sequence for which to compute the matrix profile m : int Window size T_B : ndarray The time series or sequence that contain your query subsequences of interest. Default is `None` which corresponds to a self-join. ignore_trivial : bool Set to `True` if this is a self-join. Otherwise, for AB-join, set this to `False`. Default is `True`. Returns ------- out : ndarray The first column consists of the matrix profile, the second column consists of the matrix profile indices, the third column consists of the left matrix profile indices, and the fourth column consists of the right matrix profile indices. """ metadata = metadata_base.PrimitiveMetadata({ '__author__': "DATA Lab @Texas A&M University", 'name': "Matrix Profile", #'python_path': 'd3m.primitives.tods.feature_analysis.matrix_profile', 'python_path': 'd3m.primitives.tods.detection_algorithm.matrix_profile', 'source': {'name': "DATALAB @Taxes A&M University", 'contact': 'mailto:khlai037@tamu.edu', 'uris': ['https://gitlab.com/lhenry15/tods/-/blob/Yile/anomaly-primitives/anomaly_primitives/MatrixProfile.py']}, 'algorithm_types': [metadata_base.PrimitiveAlgorithmType.MATRIX_PROFILE,], 'primitive_family': metadata_base.PrimitiveFamily.FEATURE_CONSTRUCTION, 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'MatrixProfilePrimitive')), 'hyperparams_to_tune': ['window_size'], 'version': '0.0.2', }) def __init__(self, *, hyperparams: Hyperparams) -> None: super().__init__(hyperparams=hyperparams) self._clf = MP(window_size = hyperparams['window_size']) self.primitiveNo = PrimitiveCount.primitive_no PrimitiveCount.primitive_no+=1 def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> base.CallResult[Outputs]: """ Args: inputs: Container DataFrame timeout: Default iterations: Default Returns: Container DataFrame containing Matrix Profile of selected columns """ # 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 if len(self._training_indices) > 0: 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") if not self._fitted: raise PrimitiveNotFittedError("Primitive not fitted.") sk_inputs = inputs if self.hyperparams['use_semantic_types']: sk_inputs = inputs.iloc[:, self._training_indices] output_columns = [] if len(self._training_indices) > 0: sk_output = self._clf.produce(sk_inputs) if sparse.issparse(sk_output): 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: 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) #print(outputs) #CallResult(outputs) #print("___") print(outputs.columns) #outputs.columns = [str(x) for x in outputs.columns] return CallResult(outputs) # assert isinstance(inputs, container.DataFrame), type(container.DataFrame) # _, self._columns_to_produce = self._get_columns_to_fit(inputs, self.hyperparams) # #print("columns_to_produce ", self._columns_to_produce) # outputs = inputs # if len(self._columns_to_produce) > 0: # for col in self.hyperparams['use_columns']: # output = self._clf.produce(inputs.iloc[ : ,col]) # outputs = pd.concat((outputs, pd.DataFrame({inputs.columns[col]+'_matrix_profile': output[:,0], # inputs.columns[col]+'_matrix_profile_indices': output[:,1], # inputs.columns[col]+'_left_matrix_profile_indices': output[:,2], # inputs.columns[col]+'_right_matrix_profile_indices': output[:,3]})), axis = 1) # else: # if self.hyperparams['error_on_no_input']: # raise RuntimeError("No input columns were selected") # self.logger.warn("No input columns were selected") # #print(outputs) # self._update_metadata(outputs) # return base.CallResult(outputs) def _update_metadata(self, outputs): outputs.metadata = outputs.metadata.generate(outputs) @classmethod def _get_columns_to_fit(cls, inputs: Inputs, hyperparams: Hyperparams): """ 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: """ 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(column_metadata) # print(column_metadata['structural_type'], accepted_structural_types) if not issubclass(column_metadata['structural_type'], accepted_structural_types): return False semantic_types = set(column_metadata.get('semantic_types', [])) # print(column_metadata) # print(semantic_types, accepted_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