Module PdmContext.ContextGeneration
Classes
class ContextGenerator (target, context_horizon='8 hours', Causalityfunct=<function calculate_with_pc>, mapping_functions=None, debug=False)
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This Class handle the Context Generation. It keeps an internal buffer to build the context for a target series based on the provided context_horizon. All data are passed though the collect_data method, which return a corresponding Context when target data are passed.
Parameters:
target: The name of the target source, which will be used as the baseline in order to map different samples rate to that of the target sample rate.
context_horizon: The time period to look back for context data, the form of that parameter is "8 hours"
Causalityfunct: the causality discovery method to use to produce causal relationships between context data, This must be a function with parameters two equal size lists, one with names and the other with data (a list of list or 2D numpy array).
mapping_functions: Dictionary used to associate each type with a mapping function.
User can use this dictionary to define his own mapping function and types of sources
Default value None: use default mappers for types: isolated,configuration, categorical and univariate
Default Sources Types Supported (in case of mapping_functions is None):
1) Continuous type (those that have some kind of arithmetic value) 2) Discrete events (without value) , where one of the type isolated or configuration or categorical must be assigned A guide on how to specify the type is, that events which assumed to have impact only on their occurrence, are called isolated, while others that are related to some kind of configuration with more permanent impact, are called configuration. Categorical values can be defined as categorical type Essentially the type of the events define the way that will be transformed to real values time-series.
debug: If it runs on debug mode
Expand source code
class ContextGenerator: def __init__(self, target, context_horizon="8 hours", Causalityfunct=calculate_with_pc, mapping_functions=None,debug=False): """ This Class handle the Context Generation. It keeps an internal buffer to build the context for a target series based on the provided context_horizon. All data are passed though the collect_data method, which return a corresponding Context when target data are passed. **Parameters**: **target**: The name of the target source, which will be used as the baseline in order to map different samples rate to that of the target sample rate. **context_horizon**: The time period to look back for context data, the form of that parameter is "8 hours" **Causalityfunct**: the causality discovery method to use to produce causal relationships between context data, This must be a function with parameters two equal size lists, one with names and the other with data (a list of list or 2D numpy array). **mapping_functions**: Dictionary used to associate each type with a mapping function. User can use this dictionary to define his own mapping function and types of sources Default value None: use default mappers for types: isolated,configuration, categorical and univariate Default Sources Types Supported (in case of mapping_functions is None): 1) Continuous type (those that have some kind of arithmetic value) 2) Discrete events (without value) , where one of the type isolated or configuration or categorical must be assigned A guide on how to specify the type is, that events which assumed to have impact only on their occurrence, are called isolated, while others that are related to some kind of configuration with more permanent impact, are called configuration. Categorical values can be defined as categorical type Essentially the type of the events define the way that will be transformed to real values time-series. **debug**: If it runs on debug mode """ self.debug = debug self.target = target self.contexts = [] self.causality_discovery = Causalityfunct self.buffer = [] # helpers self.type_of_series = {} self.horizon = context_horizon.split(" ")[0] if len(context_horizon.split(" ")) == 1: self.horizon_time = "hours" else: if context_horizon.split(" ")[1] in ["days", "hours", "minutes", "seconds"]: self.horizon_time = context_horizon.split(" ")[1] else: assert False, "Time horizon must be either a single number or in form of \"8 hours\" where acceptable time frames are hours,days,minutes,seconds" self.horizon = int(self.horizon) self.interpret_history_pos = 0 self.context_pos = 0 self.default_usage=False if mapping_functions is None: self.default_usage=True self.mapping_functions={ "Univariate":map_univariate_to_continuous(), "isolated": map_isolated_to_continuous(), "configuration": map_configuration_to_continuous(), "categorical":map_categorical_to_continuous() } else: self.mapping_functions=mapping_functions def collect_data(self, timestamp, source, name, value=None, type="Univariate", replace=[]): ''' This method is used when data are passed iteratively, and stored in buffer When data of target source arrive, a corresponding context is produced. Sources can be of different sample rate (all sources are mapped to the targets sample rate when context is produced) Default Sources Supported (in case of mapping_functions is None) : 1) Continuous type (those that have some kind of arithmetic value) 2) Discrete events (without value) , where one of the type isolated or configuration or categorical must be assigned A guide on how to specify the type is, that events which assumed to have impact only on their occurrence, are called isolated, while others that are related to some kind of configuration with more permanent impact, are called configuration. Categorical values can be defined as categorical type Essentially the type of the events define the way that will be transformed to real values time-series. **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data associated with the mapping function **replace**: a list of tuples, which define a replacement policy. If a tuple (e,b) exist in replace list, then when a b event is inserted, it will replace an e event with the same timestamp (if exists). **return**: structure.Context object when the data name match to the target name or None. ''' if value is None: if type not in self.mapping_functions.keys(): assert False, f"The type must be defined as one of mapping functions types: {self.mapping_functions.keys()} when no value is passed" eventpoint = Eventpoint(code=name, source=source, timestamp=timestamp, details=value, type=type) self.add_to_buffer(eventpoint, replace) if self.target == name or self.target == f"{name}@{source}": contextobject = self.generate_context(e=eventpoint, buffer=self.buffer) return contextobject else: return None def add_to_buffer(self, e: Eventpoint, replace=None): """ Adds an Event point to the buffer (keeping the buffer time ordered) """ if e.type is None: e.type="Univariate" if replace is None: replace = [] index = len(self.buffer) for i in range(len(self.buffer) - 1, 0, -1): if self.buffer[i].timestamp < e.timestamp: index = i + 1 break else: index = len(self.buffer) for i in range(len(self.buffer) - 1, 0, -1): # check for replacement if self.buffer[i].timestamp == e.timestamp: for rep in replace: if self.buffer[i].code == rep[0] and e.code == rep[1]: self.buffer[i] = e return index = i + 1 if self.buffer[i].timestamp < e.timestamp: index = i + 1 break if index == len(self.buffer): self.buffer.append(e) else: self.buffer = self.buffer[: index] + [e] + self.buffer[index:] def generate_context(self, e: Eventpoint, buffer): """ Generate context (and interpretation only when mapping function is the default). **Parameters**: **e**: Eventpoint related to the last target's data. **buffer**: A list with all Eventpoints in the time horizon """ contextcurrent, target_series_name = self.create_context(e, buffer) if self.default_usage: contextcurrent=self.create_interpretation(contextcurrent, target_series_name) else: contextcurrent.CR["interpretation"]=[] self.contexts.append(contextcurrent) i=0 pos=0 while self.contexts[i].timestamp < self.contexts[-1].timestamp - pd.Timedelta(self.horizon, self.horizon_time): pos=i i+=1 self.contexts=self.contexts[pos:] return contextcurrent def create_interpretation(self, contextcurrent: Context, target_series_name): """ This method collect all the contexts in the horizon and calls the interpretation method. **Parameters**: **contextcurrent**: The Context for which the interpretation is calculated **target_series_name**: Then name of the target variable """ causeswindow = self.contexts interpr = self.interpret(contextcurrent, causeswindow, target_series_name, self.type_of_series) contextcurrent.CR["interpretation"] = interpr return contextcurrent def interpret(self, context: Context, causeswindow, target, type_of_series): """ This method enhance the CR part of the Context with interpretation relating to the target variable. The interpretation are based on the edges extracted from the Casualty discovery. For each edge of type (seriesA -> target) in the edges we test if the seriesA interprets the target using the following rules: **if seriesA is isolated**: We check if its last occurrence is the current timestamp or the previous one. **if seriesA is configuration**: We check if the edges (seriesA -> target) appears in at least 80% contexts in the horizon (i.e. in causeswindow list). **if seriesA is continuous**: Then that means the target is related with seriesA and we add it to the interpretation. All the interpretations are tagged with a timestamp which refers to the first time of appearense of consecutive interpretations. Then the interpretation are sorted using this timestamp. This is done to provide a hierarchy to the interpretation since it may be the case, that when seriesA cause target , and SeriesB cause target, the oldest one is stronger since the SeriesB may be effect of seriesA. **Parameters**: **context**: Context Object to be interpreted **causeswindow**: list with the last horizon contexts **target**: the name of the target variable to interpret **type_of_series**: dictionary which define the type of each series (isolated,configuration or continuous) """ pairs = [(pair[0], pair[1], car) for pair, car in zip(context.CR['edges'], context.CR['characterization']) if target in pair[1]] # pairshop2 = [] # intermediates=[pair[0] for pair in pairs] # allothes=[pair for pair in context.CD.keys() if pair not in intermediates] # # for inter_pair in intermediates: # for name in allothes: # if (name,inter_pair) in context.CR["edges"]: # pairshop2.append((name,inter_pair)) interpretation = [] typeconection = [] if len(pairs) == 0: return [] for pair in pairs: if type_of_series[pair[0]] == "isolated": values1 = context.CD[pair[0]] if values1[-1] == 1: interpretation.append((pair[0], pair[1], pair[2], context.timestamp)) elif values1[-2] == 1: temptimestamp = context.timestamp lastcontext = self.contexts[max(len(self.contexts) - 2, 0)] for interpair in lastcontext.CR["interpretation"]: if interpair[0] == pair[0] and interpair[1] == pair[1]: temptimestamp = interpair[3] interpretation.append((pair[0], pair[1], pair[2], temptimestamp)) elif type_of_series[pair[0]] == "configuration": values1 = context.CD[pair[0]] occurence = 0 for q in range(len(values1)): if values1[q] == values1[-1] and values1[q] > 0: occurence = q break if occurence == len(values1) - 1: interpretation.append((pair[0], pair[1], pair[2], context.timestamp)) else: lead = len(values1) - occurence counter = 0 leadcontext = causeswindow[-lead:] for conte in leadcontext: try: pos = list(conte.CR["edges"]).index((pair[0], pair[1])) except: pos = -1 if pos != -1: if conte.CR["characterization"][pos] == pair[2]: counter += 1 if counter >= 0.8 * lead: temptimestamp = context.timestamp lastcontext = self.contexts[max(len(self.contexts) - 2, 0)] for interpair in lastcontext.CR["interpretation"]: if interpair[0] == pair[0] and interpair[1] == pair[1]: temptimestamp = interpair[3] interpretation.append((pair[0], pair[1], pair[2], temptimestamp)) # for real value series elif len(set(context.CD[pair[0]])) > 2: temptimestamp = context.timestamp lastcontext = self.contexts[max(len(self.contexts) - 2, 0)] for interpair in lastcontext.CR["interpretation"]: if interpair[0] == pair[0] and interpair[1] == pair[1]: temptimestamp = interpair[3] interpretation.append((pair[0], pair[1], pair[2], temptimestamp)) continue # sort with time finterpret = [] for pair in interpretation: finterpret.append((pair[0], pair[1], pair[2], pair[3])) finterpret.sort(key=lambda tup: tup[3]) # sorts in place return finterpret def create_context(self, current: Eventpoint, buffer): """ Transform the data collected to the buffer in to suitable form and generates the CD part of the context along with the edges and characterizations: **Steps:** **Create CD**: Parallel continuous representations of all different sources in the buffer. This step involves, matching the different sample rates of different sources to that of the target. Transform the Event sources to continuous representation. **Calculate Causality edges**: Perform Causal Discovery using the causality function, to create edges (part of CR of the Context) **Tag each edge with characterization**: for each (a,b) in the edges, a characterization of (unknown, decrease, increase), based on the type of the a. **Parameters**: **current**: The current Event of target's data which triger the context creation **buffer**: ordered list with Eventpoint of all sources. **return**: Context object. """ # start = time.time() # df with ,dt,code,source,value # Keep only last horizon events last = self.buffer[-1] pos = len(self.buffer) - 1 for i in range(len(self.buffer)): if self.buffer[i].timestamp >= (last.timestamp - pd.Timedelta(self.horizon, self.horizon_time)): pos = i break self.buffer = self.buffer[pos:] # end=time.time() # print(f"find position on buffer: {end-start}") # start = time.time() dataforcontext = buffer datatodf = [[pd.to_datetime(e.timestamp) for e in dataforcontext], [str(e.code) for e in dataforcontext], [str(e.source) for e in dataforcontext], [e.details for e in dataforcontext], [e.type for e in dataforcontext]] npcontext = np.array(datatodf) npcontext = npcontext.T npcontext = self.Numpy_preproccess(npcontext) # end = time.time() # print(f"preprocess df: {end - start}") ############# create context ############################ # start = time.time() ## collect uniqeu data series # allcodes = df['code'].unique() allcodes = np.unique(npcontext[:, 1]) allcodes = [code for code in allcodes] allcodes = set(allcodes) for uncode in allcodes: for qq in range(len(npcontext)): if uncode in npcontext[qq][1]: self.type_of_series[uncode] = npcontext[qq][4] break ## build target series target_series_name, target_series = self.build_target_series_for_context(current, npcontext) ## create series for each source (alldata) alldata = self.create_continuous_representation(target_series_name, target_series, npcontext, self.type_of_series, allcodes,self.mapping_functions) # end = time.time() # print(f"Create series: {end-start}") storing = self.calculate_edges(alldata, current.timestamp) storing["characterization"] = self.get_characterization(storing) # print(f"Calculate edges: {end - start}") # print("========================") contextpbject = Context.context_from_dict(storing) return contextpbject, target_series_name def get_characterization(self, context): """ This method calculate the characterizations of (a,b) edges to characterize the influence of a in b by one of the three characterizations: unknown, decrease, increase. The characterization is calculated differently for event and continuous data. **Parameters**: **context**: dictionary with a kye 'edges' for which a parallel list of characterizations will be created. """ edges = context["edges"] characterizations = [] for edge in edges: if self.target in edge[0]: char = self.characterize_event_continuous(context, edge) elif self.type_of_series[edge[0]] == "isolation" or self.type_of_series[edge[0]] == "configuration": char = self.characterize_event_edge(context, edge) else: char = self.characterize_event_continuous(context, edge) characterizations.append(char) return characterizations def characterize_event_edge(self, context, edge): """ This method characterizes the edge (a,b) when a is isolated or configuration event. To do this, it detects the last occurrence of a and split the data of series a and series b based on that. Then checks if the median of b after the occurrence is at larger than the median b before plus two times the standard deviation of b series before the occurrence. If that is true then is characterized as increase. Else it is checked for the opposite, (if it is smaller for at least 2 times the standard deviation of b) and characterized as decrease. Otherwise, is characterized as unknown. **Parameters**: **context**: that the edge to be characterized belongs. **edge**: A tuple (a,b) where a is isolated or configuration event. **return**: A characterization for the edge (unknown, decrease or increase) """ name1 = edge[0] name2 = edge[1] values1 = context[name1] values2 = [float(kati) for kati in context[name2]] occurence = len(values1) - 1 for i in range(len(values1) - 2, 0, -1): if values1[i] != values1[-1]: occurence = i + 1 break previusoccurence = 0 for i in range(occurence - 2, 0, -1): if values1[i] != values1[occurence - 1]: previusoccurence = i if occurence - previusoccurence < 2: # or len(values2)-occurence<2: return "unknown" values2before = values2[previusoccurence:occurence] # stdv = statistics.stdev(values2before) # mean = statistics.stdev(values2before) # values2before=[v if v<mean+5*stdv else mean for v in values2before] # values2after=values2[occurence:] values2after = [values2[-1]] # stdv = statistics.stdev(values2after) # mean = statistics.stdev(values2after) # values2after = [v if v < mean + 3 * stdv else mean for v in values2after] stdv = statistics.stdev(values2before) if len(values2before) == 0: char = "unknown" elif statistics.median(values2before) - statistics.median(values2after) > 2 * stdv: char = "decrease" elif statistics.median(values2after) - statistics.median(values2before) > 2 * stdv: char = "increase" else: char = "unknown" return char def characterize_event_continuous(self, context, edge): """ This method characterizes the edge (a,b) when a is continuous. To do this, the delta between the timestamps of the series b is calculated (i.e. the difference between current and next timestamps). If the summ of the deltas between b series data, is greater than 2 times the standard deviation of the b, then the increase characterization is returned. If the sum is lower than 2 times the standard deviation the decreased characterization is returned. Otherwise, the unknown characterization is returned. **Parameters**: **context**: that the edge to be characterized belongs. **edge**: A tuple (a,b) where a is isolated or configuration event. **return**: A characterization for the edge (unknown, decrease or increase) """ name1 = edge[0] name2 = edge[1] values1 = [float(kati) for kati in context[name1]] values2 = [float(kati) for kati in context[name2]] prev = values2[0] diff = 0 for v in values2[1:]: diff += (v - prev) if len(values2) == 0: char = "unknown" stdv = statistics.stdev(values2) if diff > 2 * stdv: char = "increase" elif diff < -2 * stdv: char = "decrease" else: char = "unknown" return char def calculate_edges(self, alldata, timestamp): """ Formulate the data in appropriate form to call self.calculate_causality which return the edges for the context. **Parameters**: **alldata**: a 2D numpy array with all series data (equivalent to the CD of the Context) **timestamp**: timestamp of the context from which the edges are calculated. **return**: a dictionary with 'edges' key (containing the calculated edges after Causality discovery). """ # start = time.time() storing = {} storing["timestamp"] = timestamp alldata_names = [nn[0] for nn in alldata] alldata_data = [nn[1] for nn in alldata] for namedd, datadd in zip(alldata_names, alldata_data): storing[namedd] = datadd # For context with more than two series calculate PC casualities count = len([1 for lista in alldata_data if lista is not None and len(set(lista)) > 1]) if count > 1 and len(alldata[0][1]) > 5: alldata_names = [nn[0] for nn in alldata if nn[1] is not None and len(set(nn[1])) > 1] alldata_data = [nn[1] for nn in alldata if nn[1] is not None and len(set(nn[1])) > 1] # end = time.time() # print(f"before {end - start}") # start=time.time() edges = self.calculate_causality(np.column_stack(alldata_data), alldata_names) # end=time.time() # print(f"actual edge calculation {end-start}") if edges is None: singleedges = [] else: singleedges = edges # print(edges) storing["edges"] = singleedges return storing storing["edges"] = [] return storing def create_continuous_representation(self, target_series_name, target_series, df, type_of_series, allcodes,mapping_functions): """ This method handles the creation of continuous representation for all type of sources observed in context, and is the first part for creating the CD of the context. Based on the type of each source, call the appropriate method to create the continuous representaiton. **Parameters**: **target_series_name**: The name of the target series. **target_series**: Used to align sample rate. **type_of_series**: A dictionary to define the type of the different sources (the type can be Isolated, Configuration, Categorical and Univariate). **allcodes**: Contain all the names for the sources we want to build the context. **mapping_functions**: Dictionary used to associate each type with a mapping function. **return**: The CD part of the context. """ windowvalues = df # .values alldata = [] alldata.append((target_series_name, [tag[0] for tag in target_series])) for name in allcodes: # already calculated in targetseries. if target_series_name in name: continue # detect the occurancies occurrences = [(value, time) for code, value, time in zip(windowvalues[:, 1], windowvalues[:, 3], windowvalues[:, 0]) if name in code] vector = [0 for i in range(len(target_series))] if len(occurrences) == 0: vector = [0 for i in range(len(target_series))] if max(vector) == 0 and min(vector) == 0: vector = None alldata.append((name, vector)) elif type_of_series[name] in mapping_functions.keys(): mapper = mapping_functions[type_of_series[name]] vectors, names = mapper.map(target_series, occurrences, name) for in_vector, new_name in zip(vectors, names): if max(in_vector) == 0 and min(in_vector) == 0: vector = None else: vector = in_vector if new_name not in self.type_of_series.keys(): self.type_of_series[new_name] = "configuration" alldata.append((new_name, vector)) else: assert False,f" No mapping function defined for type {type_of_series[name]}" return alldata def build_target_series_for_context(self, current: Eventpoint, df: np.ndarray): target_series_name = current.code windowvalues = df # .values target_series = [(value, time) for code, value, time in zip(windowvalues[:, 1], windowvalues[:, 3], windowvalues[:, 0]) if target_series_name in code] return target_series_name, target_series def Numpy_preproccess(self, npcontext): npcontext = np.where(npcontext == "nan", None, npcontext) mask = [('0_' in code and value is None) for code, value in zip(npcontext[:, 1], npcontext[:, 3])] # if isinstance(mask,collections.abc.Sequence)==False: # mask=[mask] npcontext[mask, 3] = 0 mask = [('1_' in code and value is None) for code, value in zip(npcontext[:, 1], npcontext[:, 3])] npcontext[mask, 3] = 1 code_with_source = [f"{code}{source}" if source[0] == "@" else f"{code}@{source}" for code, source in zip(npcontext[:, 1], npcontext[:, 2])] npcontext[:, 1] = code_with_source return npcontext def calculate_causality(self, dataor, names): num_time_series = len(dataor) data = np.array(dataor) edges = self.causality_discovery(names, data) # edges=self.calculatewithPc(names,data) # edges=self.calculatewith_fci(names,data) # edges=self.salesforcePC(names,data) return edges def plot(self,contexts, filteredges=None): if filteredges is None: filteredges = [["", "", ""]] show_context_list(contexts, self.target, filteredges=filteredges) def plot_interpretation(self,contexts, filteredges=None): if filteredges is None: filteredges = [["", "", ""]] show_context_interpretations(contexts, self.target, filteredges=filteredges)
Methods
def Numpy_preproccess(self, npcontext)
def add_to_buffer(self, e: Eventpoint, replace=None)
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Adds an Event point to the buffer (keeping the buffer time ordered)
def build_target_series_for_context(self, current: Eventpoint, df: numpy.ndarray)
def calculate_causality(self, dataor, names)
def calculate_edges(self, alldata, timestamp)
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Formulate the data in appropriate form to call self.calculate_causality which return the edges for the context.
Parameters:
alldata: a 2D numpy array with all series data (equivalent to the CD of the Context)
timestamp: timestamp of the context from which the edges are calculated.
return: a dictionary with 'edges' key (containing the calculated edges after Causality discovery).
def characterize_event_continuous(self, context, edge)
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This method characterizes the edge (a,b) when a is continuous. To do this, the delta between the timestamps of the series b is calculated (i.e. the difference between current and next timestamps). If the summ of the deltas between b series data, is greater than 2 times the standard deviation of the b, then the increase characterization is returned. If the sum is lower than 2 times the standard deviation the decreased characterization is returned. Otherwise, the unknown characterization is returned.
Parameters:
context: that the edge to be characterized belongs.
edge: A tuple (a,b) where a is isolated or configuration event.
return: A characterization for the edge (unknown, decrease or increase)
def characterize_event_edge(self, context, edge)
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This method characterizes the edge (a,b) when a is isolated or configuration event. To do this, it detects the last occurrence of a and split the data of series a and series b based on that. Then checks if the median of b after the occurrence is at larger than the median b before plus two times the standard deviation of b series before the occurrence. If that is true then is characterized as increase. Else it is checked for the opposite, (if it is smaller for at least 2 times the standard deviation of b) and characterized as decrease. Otherwise, is characterized as unknown.
Parameters:
context: that the edge to be characterized belongs.
edge: A tuple (a,b) where a is isolated or configuration event.
return: A characterization for the edge (unknown, decrease or increase)
def collect_data(self, timestamp, source, name, value=None, type='Univariate', replace=[])
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This method is used when data are passed iteratively, and stored in buffer When data of target source arrive, a corresponding context is produced. Sources can be of different sample rate (all sources are mapped to the targets sample rate when context is produced)
Default Sources Supported (in case of mapping_functions is None) :
1) Continuous type (those that have some kind of arithmetic value)
2) Discrete events (without value) , where one of the type isolated or configuration or categorical must be assigned A guide on how to specify the type is, that events which assumed to have impact only on their occurrence, are called isolated, while others that are related to some kind of configuration with more permanent impact, are called configuration. Categorical values can be defined as categorical type Essentially the type of the events define the way that will be transformed to real values time-series.
Parameters:
timestamp: The timestamp of the arrived value
source: The source of the arrived value
name: The name (or identifier) of the arrived value
value: The value (float), in case this is None the arrived data is considered as event
type: the type of the data associated with the mapping function
replace: a list of tuples, which define a replacement policy. If a tuple (e,b) exist in replace list, then when a b event is inserted, it will replace an e event with the same timestamp (if exists).
return: structure.Context object when the data name match to the target name or None.
def create_context(self, current: Eventpoint, buffer)
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Transform the data collected to the buffer in to suitable form and generates the CD part of the context along with the edges and characterizations:
Steps:
Create CD: Parallel continuous representations of all different sources in the buffer. This step involves, matching the different sample rates of different sources to that of the target. Transform the Event sources to continuous representation.
Calculate Causality edges: Perform Causal Discovery using the causality function, to create edges (part of CR of the Context)
Tag each edge with characterization: for each (a,b) in the edges, a characterization of (unknown, decrease, increase), based on the type of the a.
Parameters:
current: The current Event of target's data which triger the context creation
buffer: ordered list with Eventpoint of all sources.
return: Context object.
def create_continuous_representation(self, target_series_name, target_series, df, type_of_series, allcodes, mapping_functions)
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This method handles the creation of continuous representation for all type of sources observed in context, and is the first part for creating the CD of the context.
Based on the type of each source, call the appropriate method to create the continuous representaiton.
Parameters:
target_series_name: The name of the target series.
target_series: Used to align sample rate.
type_of_series: A dictionary to define the type of the different sources (the type can be Isolated, Configuration, Categorical and Univariate).
allcodes: Contain all the names for the sources we want to build the context.
mapping_functions: Dictionary used to associate each type with a mapping function.
return: The CD part of the context.
def create_interpretation(self, contextcurrent: Context, target_series_name)
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This method collect all the contexts in the horizon and calls the interpretation method.
Parameters:
contextcurrent: The Context for which the interpretation is calculated
target_series_name: Then name of the target variable
def generate_context(self, e: Eventpoint, buffer)
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Generate context (and interpretation only when mapping function is the default).
Parameters:
e: Eventpoint related to the last target's data.
buffer: A list with all Eventpoints in the time horizon
def get_characterization(self, context)
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This method calculate the characterizations of (a,b) edges to characterize the influence of a in b by one of the three characterizations: unknown, decrease, increase.
The characterization is calculated differently for event and continuous data. Parameters:
context: dictionary with a kye 'edges' for which a parallel list of characterizations will be created.
def interpret(self, context: Context, causeswindow, target, type_of_series)
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This method enhance the CR part of the Context with interpretation relating to the target variable. The interpretation are based on the edges extracted from the Casualty discovery. For each edge of type (seriesA -> target) in the edges we test if the seriesA interprets the target using the following rules:
if seriesA is isolated: We check if its last occurrence is the current timestamp or the previous one.
if seriesA is configuration: We check if the edges (seriesA -> target) appears in at least 80% contexts in the horizon (i.e. in causeswindow list).
if seriesA is continuous: Then that means the target is related with seriesA and we add it to the interpretation.
All the interpretations are tagged with a timestamp which refers to the first time of appearense of consecutive interpretations. Then the interpretation are sorted using this timestamp. This is done to provide a hierarchy to the interpretation since it may be the case, that when seriesA cause target , and SeriesB cause target, the oldest one is stronger since the SeriesB may be effect of seriesA.
Parameters:
context: Context Object to be interpreted
causeswindow: list with the last horizon contexts
target: the name of the target variable to interpret
type_of_series: dictionary which define the type of each series (isolated,configuration or continuous)
def plot(self, contexts, filteredges=None)
def plot_interpretation(self, contexts, filteredges=None)
class ContextGeneratorBatch (df_data, target, type_of_series, context_horizon='8 hours', Causalityfunct=<function calculate_with_pc>, debug=False, file_path=None)
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This version doesn't support interpretation and is created for faster experiments using Context.
df_data The data to consider in context in form of Data Frame.
target The name of the target source, which will be used as the baseline in order to map different samples
type_of_series: dictionary which define the type of each series (isolated,configuration or continuous) rate to that of the target sample rate.
context_horizon The time period to look back for context data, the form of that parameter is "8 hours"
Causalityfunct the causality discovery method to use to produce causal relationships between context data, This must be a function with parameters two equal size lists, one with names and the other with data (a list of list or 2D numpy array).
debug If it runs on debug mode
file_path Store the results of context in pickle file, considered only when it is not None.
Expand source code
class ContextGeneratorBatch(): def __init__(self,df_data,target,type_of_series,context_horizon="8 hours",Causalityfunct=calculate_with_pc,debug=False,file_path=None): """ This version doesn't support interpretation and is created for faster experiments using Context. **df_data** The data to consider in context in form of Data Frame. ** target** The name of the target source, which will be used as the baseline in order to map different samples **type_of_series**: dictionary which define the type of each series (isolated,configuration or continuous) rate to that of the target sample rate. **context_horizon** The time period to look back for context data, the form of that parameter is "8 hours" **Causalityfunct** the causality discovery method to use to produce causal relationships between context data, This must be a function with parameters two equal size lists, one with names and the other with data (a list of list or 2D numpy array). **debug** If it runs on debug mode **file_path** Store the results of context in pickle file, considered only when it is not None. """ self.file_path=file_path self.debug=debug self.target=target self.df_data=df_data self.contexts=self._load_contexts() self.causality_discovery=Causalityfunct self.buffer=[] #helpers self.type_of_series = type_of_series self.horizon = context_horizon.split(" ")[0] if len(context_horizon.split(" ")) == 1: self.horizon_time = "hours" else: if context_horizon.split(" ")[1] in ["days", "hours", "minutes", "seconds"]: self.horizon_time = context_horizon.split(" ")[1] else: assert False, "Time horizon must be either a single number or in form of \"8 hours\" where acceptable time frames are hours,days,minutes,seconds" self.horizon = int(self.horizon) self.interpret_history_pos = 0 self.context_pos = 0 def generate_context(self,datetime_index): contextpbject, target_series_name,time_index=self.create_context(datetime_index) self.contexts[time_index]=contextpbject return contextpbject def _save_contexts(self): if self.file_path is None: return with open(self.file_path, 'wb') as f: pickle.dump(self.contexts, f) def _load_contexts(self): if self.file_path is None: return {} my_file = Path(self.file_path) if my_file.is_file(): with open(self.file_path, 'rb') as f: return pickle.load(f) else: return {} def create_context(self, datetime_index): start_index = datetime_index - pd.Timedelta(self.horizon, self.horizon_time) context=self.df_data.loc[start_index:datetime_index] allcodes = np.unique(self.df_data.columns) allcodes = [code for code in allcodes] allcodes = set(allcodes) ## build target series target_series_name= self.target target_series=context[self.target].values ## create series for each source (alldata) alldata = [] for col in context.columns: alldata.append((col, context[col].values)) # end = time.time() # print(f"Create series: {end-start}") storing = self.calculate_edges(alldata, context.index[-1]) storing["characterization"] = self.getcaracterize(storing) # print(f"Calculate edges: {end - start}") # print("========================") contextpbject = Context.context_from_dict(storing) return contextpbject, target_series_name,context.index[-1] def calculate_edges(self,alldata, timestamp): #start = time.time() storing = {} storing["timestamp"] = timestamp alldata_names = [nn[0] for nn in alldata] alldata_data = [nn[1] for nn in alldata] for namedd, datadd in zip(alldata_names, alldata_data): storing[namedd] = datadd alldata_names = [nn[0] for nn in alldata if nn[1] is not None and len(set(nn[1])) > 1] alldata_data = [nn[1] for nn in alldata if nn[1] is not None and len(set(nn[1])) > 1] # For context with more than two series calculate PC casualities count = len([1 for lista in alldata_data if lista is not None]) if count > 1: if len(alldata[0][1]) > 5: # alldata_names = [nn[0] for nn in alldata if nn[1] is not None and len(set(nn[1]))>1] # alldata_data = [nn[1] for nn in alldata if nn[1] is not None and len(set(nn[1]))>1] #end = time.time() #print(f"before {end - start}") #start=time.time() if len(alldata) <= 1: edges = [] else: edges = self.calculate_causality(np.column_stack(alldata_data), alldata_names) #end=time.time() #print(f"actual edge calculation {end-start}") if edges is None: singleedges = [] else: singleedges = edges # print(edges) storing["edges"] = singleedges return storing storing["edges"] = [] return storing def calculate_causality(self, dataor, names): num_time_series = len(dataor) data = np.array(dataor) edges = self.causality_discovery(names, data) # edges=self.calculatewithPc(names,data) # edges=self.calculatewith_fci(names,data) # edges=self.salesforcePC(names,data) return edges def getcaracterize(self,context): edges = context["edges"] characterizations = [] for edge in edges: name1 = edge[0] name2 = edge[1] values1 = context[name1] values2 = [float(kati) for kati in context[name2]] occurence=len(values1)-1 for i in range(len(values1)-2,0,-1): if values1[i] != values1[-1]: occurence = i+1 break previusoccurence = 0 for i in range(occurence-2,0,-1): if values1[i] != values1[occurence-1]: previusoccurence = i if occurence - previusoccurence < 2: # or len(values2)-occurence<2: characterizations.append("uknown") continue values2before = values2[previusoccurence:occurence] # stdv = statistics.stdev(values2before) # mean = statistics.stdev(values2before) # values2before=[v if v<mean+5*stdv else mean for v in values2before] # values2after=values2[occurence:] values2after = [values2[-1]] # stdv = statistics.stdev(values2after) # mean = statistics.stdev(values2after) # values2after = [v if v < mean + 3 * stdv else mean for v in values2after] stdv = statistics.stdev(values2before) if len(values2before) == 0: char = "uknown" elif statistics.median(values2before) - statistics.median(values2after) > 2 * stdv: char = "decrease" elif statistics.median(values2after) - statistics.median(values2before) > 2 * stdv: char = "increase" else: char = "uknown" characterizations.append(char) return characterizations def __del__(self): if self.file_path is None: return my_file = Path(self.file_path) if my_file.is_file(): return else: self._save_contexts()
Methods
def calculate_causality(self, dataor, names)
def calculate_edges(self, alldata, timestamp)
def create_context(self, datetime_index)
def generate_context(self, datetime_index)
def getcaracterize(self, context)