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 numpy as np from keras.models import Sequential from keras.layers import Dense, Dropout , LSTM from keras.regularizers import l2 from keras.losses import mean_squared_error from sklearn.preprocessing import StandardScaler from sklearn.utils import check_array from sklearn.utils.validation import check_is_fitted from pyod.utils.stat_models import pairwise_distances_no_broadcast from pyod.models.base import BaseDetector # Custom import commands if any import warnings import numpy as np from sklearn.utils import check_array from sklearn.exceptions import NotFittedError # from numba import njit from pyod.utils.utility import argmaxn 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.supervised_learning import SupervisedLearnerPrimitiveBase from d3m.primitive_interfaces.unsupervised_learning import UnsupervisedLearnerPrimitiveBase from d3m.primitive_interfaces.transformer import TransformerPrimitiveBase from d3m.primitive_interfaces.base import ProbabilisticCompositionalityMixin, ContinueFitMixin from d3m import exceptions import pandas import uuid from d3m import container, utils as d3m_utils from detection_algorithm.UODBasePrimitive import Params_ODBase, Hyperparams_ODBase, UnsupervisedOutlierDetectorBase __all__ = ('DeepLog',) Inputs = container.DataFrame Outputs = container.DataFrame class Params(Params_ODBase): ######## Add more Attributes ####### pass class Hyperparams(Hyperparams_ODBase): hidden_size = hyperparams.Hyperparameter[int]( default=64, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="hidden state dimension" ) loss = hyperparams.Hyperparameter[typing.Union[str, None]]( default='mean_squared_error', semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="loss function" ) optimizer = hyperparams.Hyperparameter[typing.Union[str, None]]( default='Adam', semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Optimizer" ) epochs = hyperparams.Hyperparameter[int]( default=10, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Epoch" ) batch_size = hyperparams.Hyperparameter[int]( default=32, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Batch size" ) dropout_rate = hyperparams.Hyperparameter[float]( default=0.2, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Dropout rate" ) l2_regularizer = hyperparams.Hyperparameter[float]( default=0.1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="l2 regularizer" ) validation_size = hyperparams.Hyperparameter[float]( default=0.1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="validation size" ) window_size = hyperparams.Hyperparameter[int]( default=1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="window size" ) features = hyperparams.Hyperparameter[int]( default=1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Number of features in Input" ) stacked_layers = hyperparams.Hyperparameter[int]( default=1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Number of LSTM layers between input layer and Final Dense Layer" ) preprocessing = hyperparams.UniformBool( default=True, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="Whether to Preprosses the data" ) verbose = hyperparams.Hyperparameter[int]( default=1, semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'], description="verbose" ) contamination = hyperparams.Uniform( lower=0., upper=0.5, default=0.1, description='the amount of contamination of the data set, i.e.the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function', semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter'] ) class DeepLogPrimitive(UnsupervisedOutlierDetectorBase[Inputs, Outputs, Params, Hyperparams]): """ A primitive that uses DeepLog for outlier detection Parameters ---------- """ __author__ = "DATA Lab at Texas A&M University", metadata = metadata_base.PrimitiveMetadata( { '__author__': "DATA Lab @Texas A&M University", 'name': "DeepLog Anomolay Detection", 'python_path': 'd3m.primitives.tods.detection_algorithm.deeplog', '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.DEEPLOG], 'primitive_family': metadata_base.PrimitiveFamily.ANOMALY_DETECTION, 'id': str(uuid.uuid3(uuid.NAMESPACE_DNS, 'DeepLogPrimitive')), 'hyperparams_to_tune': ['hidden_size', 'loss', 'optimizer', 'epochs', 'batch_size', 'l2_regularizer', 'validation_size', 'window_size', 'features', 'stacked_layers', 'preprocessing', 'verbose', 'dropout_rate','contamination'], 'version': '0.0.1', } ) 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 = DeeplogLstm(hidden_size=hyperparams['hidden_size'], loss=hyperparams['loss'], optimizer=hyperparams['optimizer'], epochs=hyperparams['epochs'], batch_size=hyperparams['batch_size'], dropout_rate=hyperparams['dropout_rate'], l2_regularizer=hyperparams['l2_regularizer'], validation_size=hyperparams['validation_size'], window_size=hyperparams['window_size'], stacked_layers=hyperparams['stacked_layers'], preprocessing=hyperparams['preprocessing'], verbose=hyperparams['verbose'], contamination=hyperparams['contamination'] ) def set_training_data(self, *, inputs: Inputs) -> None: """ Set training data for outlier detection. Args: inputs: Container DataFrame Returns: None """ super().set_training_data(inputs=inputs) def fit(self, *, timeout: float = None, iterations: int = None) -> CallResult[None]: """ Fit model with training data. Args: *: Container DataFrame. Time series data up to fit. Returns: None """ return super().fit() def produce(self, *, inputs: Inputs, timeout: float = None, iterations: int = None) -> CallResult[Outputs]: """ Process the testing data. Args: inputs: Container DataFrame. Time series data up to outlier detection. Returns: Container DataFrame 1 marks Outliers, 0 marks normal. """ return super().produce(inputs=inputs, timeout=timeout, iterations=iterations) def get_params(self) -> Params: """ Return parameters. Args: None Returns: class Params """ return super().get_params() def set_params(self, *, params: Params) -> None: """ Set parameters for outlier detection. Args: params: class Params Returns: None """ super().set_params(params=params) class DeeplogLstm(BaseDetector): """Class to Implement Deep Log LSTM based on "https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf Only Parameter Value anomaly detection layer has been implemented for time series data""" def __init__(self, hidden_size : int = 64, optimizer : str ='adam',loss=mean_squared_error,preprocessing=True, epochs : int =100, batch_size : int =32, dropout_rate : float =0.0, l2_regularizer : float =0.1, validation_size : float =0.1, window_size: int = 1, stacked_layers: int = 1, verbose : int = 1, contamination:int = 0.001): super(DeeplogLstm, self).__init__(contamination=contamination) self.hidden_size = hidden_size self.loss = loss self.optimizer = optimizer self.epochs = epochs self.batch_size = batch_size self.dropout_rate = dropout_rate self.l2_regularizer = l2_regularizer self.validation_size = validation_size self.window_size = window_size self.stacked_layers = stacked_layers self.preprocessing = preprocessing self.verbose = verbose self.dropout_rate = dropout_rate self.contamination = contamination def _build_model(self): """ Builds Stacked LSTM model. Args: inputs : Self object containing model parameters Returns: return : model """ model = Sequential() #InputLayer model.add(LSTM(self.hidden_size,input_shape = (self.window_size,self.n_features_),return_sequences=True,dropout = self.dropout_rate)) #stacked layer for layers in range(self.stacked_layers): if(layers == self.stacked_layers -1 ): model.add(LSTM(self.hidden_size, return_sequences=False,dropout = self.dropout_rate)) continue model.add(LSTM(self.hidden_size,return_sequences=True,dropout = self.dropout_rate)) #output layer model.add(Dense(self.n_features_)) # Compile model model.compile(loss=self.loss, optimizer=self.optimizer) if self.verbose >= 1: print(model.summary()) return model def fit(self,X,y=None): """ Fit data to LSTM model. Args: inputs : X , ndarray of size (number of sample,features) Returns: return : self object with trained model """ X = check_array(X) self._set_n_classes(y) self.n_samples_, self.n_features_ = X.shape[0], X.shape[1] X_train,Y_train = self._preprocess_data_for_LSTM(X) self.model_ = self._build_model() self.history_ = self.model_.fit(X_train, Y_train, epochs=self.epochs, batch_size=self.batch_size, validation_split=self.validation_size, verbose=self.verbose).history pred_scores = np.zeros(X.shape) pred_scores[self.window_size:] = self.model_.predict(X_train) Y_train_for_decision_scores = np.zeros(X.shape) Y_train_for_decision_scores[self.window_size:] = Y_train self.decision_scores_ = pairwise_distances_no_broadcast(Y_train_for_decision_scores, pred_scores) self._process_decision_scores() return self def _preprocess_data_for_LSTM(self,X): """ Preposses data and prepare sequence of data based on number of samples needed in a window Args: inputs : X , ndarray of size (number of sample,features) Returns: return : X , Y X being samples till (t-1) of data and Y the t time data """ if self.preprocessing: self.scaler_ = StandardScaler() X_norm = self.scaler_.fit_transform(X) else: X_norm = np.copy(X) X_data = [] Y_data = [] for index in range(X.shape[0] - self.window_size): X_data.append(X_norm[index:index+self.window_size]) Y_data.append(X_norm[index+self.window_size]) X_data = np.asarray(X_data) Y_data = np.asarray(Y_data) return X_data,Y_data def decision_function(self, X): """Predict raw anomaly score of X using the fitted detector. The anomaly score of an input sample is computed based on different detector algorithms. . Parameters ---------- X : numpy array of shape (n_samples, n_features) The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns ------- anomaly_scores : numpy array of shape (n_samples,) The anomaly score of the input samples. """ check_is_fitted(self, ['model_', 'history_']) X = check_array(X) print("inside") print(X.shape) print(X[0]) X_norm,Y_norm = self._preprocess_data_for_LSTM(X) pred_scores = np.zeros(X.shape) pred_scores[self.window_size:] = self.model_.predict(X_norm) Y_norm_for_decision_scores = np.zeros(X.shape) Y_norm_for_decision_scores[self.window_size:] = Y_norm return pairwise_distances_no_broadcast(Y_norm_for_decision_scores, pred_scores)