Source code for pecos.monitoring

"""
The monitoring module contains the PerformanceMonitoring class used to run
quality control tests and store results.  The module also contains individual 
functions that can be used to run quality control tests.
"""
import pandas as pd
import numpy as np
import datetime
import logging

none_list = ['','none','None','NONE', None, [], {}]
NoneType = type(None)

logger = logging.getLogger(__name__)

def _documented_by(original, include_metadata=False):
    def wrapper(target):
        docstring = original.__doc__
        old = """
        Parameters
        ----------
        """
        new = """
        Parameters
        ----------
        data : pandas DataFrame
            Data used in the quality control test, indexed by datetime
            
        """
        if include_metadata:
           new_docstring = docstring.replace(old, new) + \
        """   
        Returns    
        ----------
        dictionary
            Results include cleaned data, mask, test results summary, and metadata
        """
        else:
            new_docstring = docstring.replace(old, new) + \
        """   
        Returns    
        ----------
        dictionary
            Results include cleaned data, mask, and test results summary
        """

        target.__doc__ = new_docstring
        return target
    return wrapper

### Object-oriented approach
[docs] class PerformanceMonitoring(object): def __init__(self): """ PerformanceMonitoring class """ self.df = pd.DataFrame() self.trans = {} self.tfilter = pd.Series(dtype='float64') self.test_results = pd.DataFrame(columns=['Variable Name', 'Start Time', 'End Time', 'Timesteps', 'Error Flag']) self.test_results['Start Time'] = self.test_results['Start Time'].astype('datetime64[ns]') self.test_results['End Time'] = self.test_results['End Time'].astype('datetime64[ns]') self.test_results['Timesteps'] = self.test_results['Timesteps'].astype('int64') @property def data(self): """ Data used in quality control analysis, added to the PerformanceMonitoring object using ``add_dataframe``. """ return self.df @property def mask(self): """ Boolean mask indicating if data that failed a quality control test. True = data point pass all tests, False = data point did not pass at least one test. """ if self.df.empty: logger.info("Empty database") return # True = pass, False = fail mask = pd.DataFrame(True, index=self.df.index, columns=self.df.columns) for i in self.test_results.index: variable = self.test_results.loc[i, 'Variable Name'] start_date = self.test_results.loc[i, 'Start Time'] end_date = self.test_results.loc[i, 'End Time'] if variable in mask.columns: try: mask.loc[start_date:end_date,variable] = False except: pass elif self.test_results.loc[i, 'Error Flag'] == 'Missing timestamp': mask.loc[start_date:end_date,:] = False return mask @property def cleaned_data(self): """ Cleaned data set, data that failed a quality control test are replaced by NaN. """ return self.df[self.mask] def _setup_data(self, key): """ Setup data to use in the quality control test """ if self.df.empty: logger.info("Empty database") return # Isolate subset if key is not None if key is not None: try: df = self.df[self.trans[key]] # copy is not needed except: logger.warning("Undefined key: " + key) return else: df = self.df.copy() return df def _generate_test_results(self, df, bound, min_failures, error_prefix): """ Compare DataFrame to bounds to generate a True/False mask where True = passed, False = failed. Append results to test_results. """ # Lower Bound if bound[0] not in none_list: mask = ~(df < bound[0]) # True = passed test error_msg = error_prefix+' < lower bound, '+str(bound[0]) self._append_test_results(mask, error_msg, min_failures) # Upper Bound if bound[1] not in none_list: mask = ~(df > bound[1]) # True = passed test error_msg = error_prefix+' > upper bound, '+str(bound[1]) self._append_test_results(mask, error_msg, min_failures) def _append_test_results(self, mask, error_msg, min_failures=1, timestamp_test=False): """ Append QC results to the PerformanceMonitoring object. Parameters ---------- mask : pandas DataFrame Result from quality control test, boolean values error_msg : string Error message to store with the QC results min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 timestamp_test : boolean, optional When True, the mask comes from a timestamp test, and the variable name should not be included in the test results """ if not self.tfilter.empty: mask[~self.tfilter] = True if mask.sum(axis=1).sum(axis=0) == mask.shape[0]*mask.shape[1]: return # The mask is translated and then converted to an np array to improve performace. # Values are reversed (T/F) to find blocks where quality control tests failed. np_mask = ~mask.T.values start_nans_mask = np.hstack( (np.resize(np_mask[:,0],(mask.shape[1],1)), np.logical_and(np.logical_not(np_mask[:,:-1]), np_mask[:,1:]))) stop_nans_mask = np.hstack( (np.logical_and(np_mask[:,:-1], np.logical_not(np_mask[:,1:])), np.resize(np_mask[:,-1], (mask.shape[1],1)))) start_col_idx, start_row_idx = np.where(start_nans_mask) stop_col_idx, stop_row_idx = np.where(stop_nans_mask) block = {'Start Row': list(start_row_idx), 'Start Col': list(start_col_idx), 'Stop Row': list(stop_row_idx), 'Stop Col': list(stop_col_idx)} # Extract test results from each block counter=0 test_results = {} for i in range(len(block['Start Col'])): timesteps = block['Stop Row'][i] - block['Start Row'][i] + 1 if timesteps >= min_failures: if timestamp_test: var_name = '' else: var_name = mask.iloc[:,block['Start Col'][i]].name start_time = mask.index[block['Start Row'][i]] end_time = mask.index[block['Stop Row'][i]] test_results[counter] = {'Variable Name': var_name, 'Start Time': start_time, 'End Time': end_time, 'Timesteps': timesteps, 'Error Flag': error_msg} counter = counter + 1 test_results = pd.DataFrame(test_results).T self.test_results = pd.concat([self.test_results, test_results], ignore_index=True)
[docs] def add_dataframe(self, data): """ Add data to the PerformanceMonitoring object Parameters ----------- data : pandas DataFrame Data to add to the PerformanceMonitoring object, indexed by datetime """ assert isinstance(data, pd.DataFrame), 'data must be of type pd.DataFrame' assert isinstance(data.index, pd.core.indexes.datetimes.DatetimeIndex), 'data.index must be a DatetimeIndex' if self.df is not None: self.df = data.combine_first(self.df) else: self.df = data.copy() # Add identity 1:1 translation dictionary trans = {} for col in data.columns: trans[col] = [col] self.add_translation_dictionary(trans)
[docs] def add_translation_dictionary(self, trans): """ Add translation dictionary to the PerformanceMonitoring object Parameters ----------- trans : dictionary Translation dictionary """ assert isinstance(trans, dict), 'trans must be of type dictionary' for key, values in trans.items(): self.trans[key] = [] for value in values: self.trans[key].append(value)
[docs] def add_time_filter(self, time_filter): """ Add a time filter to the PerformanceMonitoring object Parameters ---------- time_filter : pandas DataFrame with a single column or pandas Series Time filter containing boolean values for each time index True = keep time index in the quality control results. False = remove time index from the quality control results. """ assert isinstance(time_filter, (pd.Series, pd.DataFrame)), 'time_filter must be of type pd.Series or pd.DataFrame' if isinstance(time_filter, pd.DataFrame) and (time_filter.shape[1] == 1): self.tfilter = time_filter.squeeze() else: self.tfilter = time_filter
[docs] def check_timestamp(self, frequency, expected_start_time=None, expected_end_time=None, min_failures=1, exact_times=True): """ Check time series for missing, non-monotonic and duplicate timestamps Parameters ---------- frequency : int or float Expected time series frequency, in seconds expected_start_time : Timestamp, optional Expected start time. If not specified, the minimum timestamp is used expected_end_time : Timestamp, optional Expected end time. If not specified, the maximum timestamp is used min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 exact_times : bool, optional Controls how missing times are checked. If True, times are expected to occur at regular intervals (specified in frequency) and the DataFrame is reindexed to match the expected frequency. If False, times only need to occur once or more within each interval (specified in frequency) and the DataFrame is not reindexed. """ assert isinstance(frequency, (int, float)), 'frequency must be of type int or float' assert isinstance(expected_start_time, (NoneType, pd.Timestamp)), 'expected_start_time must be None or of type pd.Timestamp' assert isinstance(expected_end_time, (NoneType, pd.Timestamp)), 'expected_end_time must be None or of type pd.Timestamp' assert isinstance(min_failures, int), 'min_failures must be of type int' assert isinstance(exact_times, bool), 'exact_times must be of type bool' logger.info("Check timestamp") if self.df.empty: logger.info("Empty database") return if expected_start_time is None: expected_start_time = min(self.df.index) if expected_end_time is None: expected_end_time = max(self.df.index) rng = pd.date_range(start=expected_start_time, end=expected_end_time, freq=str(int(frequency*1e3)) + 'ms') # milliseconds # Check to see if timestamp is monotonic # mask = pd.TimeSeries(self.df.index).diff() < 0 mask = ~(pd.Series(self.df.index).diff() < pd.Timedelta('0 days 00:00:00')) mask.index = self.df.index mask[mask.index[0]] = True mask = pd.DataFrame(mask) mask.columns = [0] self._append_test_results(mask, 'Nonmonotonic timestamp', timestamp_test=True, min_failures=min_failures) # If not monotonically increasing, sort df by timestamp if not self.df.index.is_monotonic_increasing: self.df = self.df.sort_index() # Check for duplicate timestamps # mask = pd.TimeSeries(self.df.index).diff() == 0 mask = ~(pd.Series(self.df.index).diff() == pd.Timedelta('0 days 00:00:00')) mask.index = self.df.index mask[mask.index[0]] = True mask = pd.DataFrame(mask) mask.columns = [0] mask['TEMP'] = mask.index # remove duplicates in the mask mask.drop_duplicates(subset='TEMP', keep='last', inplace=True) del mask['TEMP'] # Drop duplicate timestamps (this has to be done before the # results are appended) self.df['TEMP'] = self.df.index #self.df.drop_duplicates(subset='TEMP', take_last=False, inplace=True) self.df.drop_duplicates(subset='TEMP', keep='first', inplace=True) self._append_test_results(mask, 'Duplicate timestamp', timestamp_test=True, min_failures=min_failures) del self.df['TEMP'] if exact_times: temp = pd.Index(rng) missing = temp.difference(self.df.index).tolist() # reindex DataFrame self.df = self.df.reindex(index=rng) mask = pd.DataFrame(data=self.df.shape[0]*[True], index=self.df.index) mask.loc[missing] = False self._append_test_results(mask, 'Missing timestamp', timestamp_test=True, min_failures=min_failures) else: # uses pandas >= 0.18 resample syntax df_index = pd.DataFrame(index=self.df.index) df_index[0]=1 # populate with placeholder values mask = ~(df_index.resample(str(int(frequency*1e3))+'ms').count() == 0) # milliseconds self._append_test_results(mask, 'Missing timestamp', timestamp_test=True, min_failures=min_failures)
[docs] def check_range(self, bound, key=None, min_failures=1): """ Check for data that is outside expected range Parameters ---------- bound : list of floats [lower bound, upper bound], None can be used in place of a lower or upper bound key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(bound, list), 'bound must be of type list' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(min_failures, int), 'min_failures must be of type int' logger.info("Check for data outside expected range") df = self._setup_data(key) if df is None: return error_prefix = 'Data' self._generate_test_results(df, bound, min_failures, error_prefix)
[docs] def check_increment(self, bound, key=None, increment=1, absolute_value=True, min_failures=1): """ Check data increments using the difference between values Parameters ---------- bound : list of floats [lower bound, upper bound], None can be used in place of a lower or upper bound key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. increment : int, optional Time step shift used to compute difference, default = 1 absolute_value : boolean, optional Use the absolute value of the increment data, default = True min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(bound, list), 'bound must be of type list' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(increment, int), 'increment must be of type int' assert isinstance(absolute_value, bool), 'absolute_value must be of type bool' assert isinstance(min_failures, int), 'min_failures must be of type int' logger.info("Check for data increment outside expected range") df = self._setup_data(key) if df is None: return if df.isnull().all().all(): logger.warning("Check increment range failed (all data is Null): " + key) return # Compute interval if absolute_value: df = np.abs(df.diff(periods=increment)) else: df = df.diff(periods=increment) if absolute_value: error_prefix = '|Increment|' else: error_prefix = 'Increment' self._generate_test_results(df, bound, min_failures, error_prefix)
[docs] def check_delta(self, bound, window, key=None, direction=None, min_failures=1): """ Check for stagnant data and/or abrupt changes in the data using the difference between max and min values (delta) within a rolling window Parameters ---------- bound : list of floats [lower bound, upper bound], None can be used in place of a lower or upper bound window : int or float Size of the rolling window (in seconds) used to compute delta key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. direction : str, optional Options = 'positive', 'negative', or None * If direction is positive, then only identify positive deltas (the min occurs before the max) * If direction is negative, then only identify negative deltas (the max occurs before the min) * If direction is None, then identify both positive and negative deltas min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(bound, list), 'bound must be of type list' assert isinstance(window, (int, float)), 'window must be of type int or float' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert direction in [None, 'positive', 'negative'], "direction must None or the string 'positive' or 'negative'" assert isinstance(min_failures, int), 'min_failures must be of type int' assert self.df.index.is_monotonic_increasing, 'index must be monotonically increasing' logger.info("Check for stagant data and/or abrupt changes using delta (max-min) within a rolling window") df = self._setup_data(key) if df is None: return window_str = str(int(window*1e3)) + 'ms' # milliseconds min_df = df.rolling(window_str, min_periods=2, closed='both').min() max_df = df.rolling(window_str, min_periods=2, closed='both').max() diff_df = max_df - min_df diff_df.loc[diff_df.index[0]:diff_df.index[0]+pd.Timedelta(window_str),:] = None def update_mask(mask1, df, window_str, bound, direction): # While the mask flags data at the time at which the failure occurs, # the actual timespan betwen the min and max should be flagged so that # the final results include actual data points that caused the failure. # This function uses numpy arrays to improve performance and returns # a mask DataFrame. mask2 = np.ones((len(mask1.index), len(mask1.columns)), dtype=bool) index = mask1.index # Loop over t, col in mask1 where condition is True for t,col in list(mask1[mask1 == 0].stack().index): icol = mask1.columns.get_loc(col) it = mask1.index.get_loc(t) t1 = t-pd.Timedelta(window_str) if (bound == 'lower') and (direction is None): # set the entire time interval to True mask2[(index >= t1) & (index <= t),icol] = False else: # extract the min and max time min_time = df.loc[t1:t,col].idxmin() max_time = df.loc[t1:t,col].idxmax() if bound == 'lower': # bound = upper, direction = positive or negative # set the entire time interval to True if (direction == 'positive') and (min_time <= max_time): mask2[(index >= t1) & (index <= t),icol] = False elif (direction == 'negative') and (min_time >= max_time): mask2[(index >= t1) & (index <= t),icol] = False elif bound == 'upper': # bound = upper, direction = None, positive or negative # set the initially flaged location to False mask2[it,icol] = True # set the time between max/min or min/max to true if min_time < max_time and (direction is None or direction == 'positive'): mask2[(index >= min_time) & (index <= max_time),icol] = False elif min_time > max_time and (direction is None or direction == 'negative'): mask2[(index >= max_time) & (index <= min_time),icol] = False elif min_time == max_time: mask2[it,icol] = False mask2 = pd.DataFrame(mask2, columns=mask1.columns, index=mask1.index) return mask2 if direction == 'positive': error_prefix = 'Delta (+)' elif direction == 'negative': error_prefix = 'Delta (-)' else: error_prefix = 'Delta' # Lower Bound if bound[0] not in none_list: mask = ~(diff_df < bound[0]) error_msg = error_prefix+' < lower bound, '+str(bound[0]) if not self.tfilter.empty: mask[~self.tfilter] = True mask = update_mask(mask, df, window_str, 'lower', direction) self._append_test_results(mask, error_msg, min_failures) # Upper Bound if bound[1] not in none_list: mask = ~(diff_df > bound[1]) error_msg = error_prefix+' > upper bound, '+str(bound[1]) if not self.tfilter.empty: mask[~self.tfilter] = True mask = update_mask(mask, df, window_str, 'upper', direction) self._append_test_results(mask, error_msg, min_failures)
[docs] def check_outlier(self, bound, window=None, key=None, absolute_value=False, streaming=False, min_failures=1): """ Check for outliers using normalized data within a rolling window The upper and lower bounds are specified in standard deviations. Data normalized using (data-mean)/std. Parameters ---------- bound : list of floats [lower bound, upper bound], None can be used in place of a lower or upper bound window : int or float, optional Size of the rolling window (in seconds) used to normalize data, If window is set to None, data is normalized using the entire data sets mean and standard deviation (column by column). default = None. key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. absolute_value : boolean, optional Use the absolute value the normalized data, default = True streaming : boolean, optional Indicates if streaming analysis should be used, default = False min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(bound, list), 'bound must be of type list' assert isinstance(window, (NoneType, int, float)), 'window must be None or of type int or float' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(absolute_value, bool), 'absolute_value must be of type bool' assert isinstance(streaming, bool), 'streaming must be of type bool' assert isinstance(min_failures, int), 'min_failures must be type int' assert self.df.index.is_monotonic_increasing, 'index must be monotonically increasing' def outlier(data_pt, history): mean = history.mean() std = history.std() zt = (data_pt - mean)/std zt.replace([np.inf, -np.inf], np.nan, inplace=True) # True = pass, False = fail if absolute_value: zt = abs(zt) mask = pd.Series(True, index=zt.index) if bound[0] not in none_list: mask = mask & (zt >= bound[0]) if bound[1] not in none_list: mask = mask & (zt <= bound[1]) return mask, zt logger.info("Check for outliers") df = self._setup_data(key) if df is None: return if absolute_value: error_prefix = '|Outlier|' else: error_prefix = 'Outlier' if streaming: metadata = self.check_custom_streaming(outlier, window, rebase=0.5, min_failures=min_failures, error_message=error_prefix) else: # Compute normalized data if window is not None: window_str = str(int(window*1e3)) + 'ms' # milliseconds df_mean = df.rolling(window_str, min_periods=2, closed='both').mean() df_std = df.rolling(window_str, min_periods=2, closed='both').std() df = (df - df_mean)/df_std else: df = (df - df.mean())/df.std() df.replace([np.inf, -np.inf], np.nan, inplace=True) if absolute_value: df = np.abs(df) #df[df.index[0]:df.index[0]+datetime.timedelta(seconds=window)] = np.nan self._generate_test_results(df, bound, min_failures, error_prefix)
[docs] def check_missing(self, key=None, min_failures=1): """ Check for missing data Parameters ---------- key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(min_failures, int), 'min_failures must be type int' logger.info("Check for missing data") df = self._setup_data(key) if df is None: return # Extract missing data mask = ~pd.isnull(df) # checks for np.nan, np.inf, True = passed test # Check to see if the missing data was already flagged as a missing timestamp missing_timestamps = self.test_results[ self.test_results['Error Flag'] == 'Missing timestamp'] for index, row in missing_timestamps.iterrows(): mask.loc[row['Start Time']:row['End Time']] = True self._append_test_results(mask, 'Missing data', min_failures=min_failures)
[docs] def check_corrupt(self, corrupt_values, key=None, min_failures=1): """ Check for corrupt data Parameters ---------- corrupt_values : list of int or floats List of corrupt data values key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 """ assert isinstance(corrupt_values, list), 'corrupt_values must be of type list' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(min_failures, int), 'min_failures must be type int' logger.info("Check for corrupt data") df = self._setup_data(key) if df is None: return # Extract corrupt data mask = ~df.isin(corrupt_values) # True = passed test # Replace corrupt data with NaN self.df[~mask] = np.nan self._append_test_results(mask, 'Corrupt data', min_failures=min_failures)
[docs] def check_custom_static(self, quality_control_func, key=None, min_failures=1, error_message=None): """ Use custom functions that operate on the entire dataset at once to perform quality control analysis Parameters ---------- quality_control_func : function Function that operates on self.df and returns a mask and metadata key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 error_message : str, optional Error message """ assert callable(quality_control_func), 'quality_control_func must be a callable function' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(min_failures, int), 'min_failures must be type int' assert isinstance(error_message, (NoneType, str)), 'error_message must be None or of type string' df = self._setup_data(key) if df is None: return # Function that operates on the entire dataset and returns a mask and # metadata for the entire dataset mask, metadata = quality_control_func(df) assert isinstance(mask, pd.DataFrame), 'mask returned by quality_control_func must be of type pd.DataFrame' assert isinstance(metadata, pd.DataFrame), 'metadata returned by quality_control_func must be of type pd.DataFrame' # Function that modifies the mask #if post_process_func is not None: # mask = post_process_func(mask) self._append_test_results(mask, error_message, min_failures) return metadata
[docs] def check_custom_streaming(self, quality_control_func, window, key=None, rebase=None, min_failures=1, error_message=None): """ Check for anomolous data using a streaming framework which removes anomolous data from the history after each timestamp. A custom quality control function is supplied by the user to determine if the data is anomolous. Parameters ---------- quality_control_func : function Function that determines if the last data point is normal or anomalous. Returns a mask and metadata for the last data point. window : int or float Size of the rolling window (in seconds) used to define history If window is set to None, data is normalized using the entire data sets mean and standard deviation (column by column). key : string, optional Data column name or translation dictionary key. If not specified, all columns are used in the test. rebase : int, float, or None Value between 0 and 1 that indicates the fraction of default = None. min_failures : int, optional Minimum number of consecutive failures required for reporting, default = 1 error_message : str, optional Error message """ assert callable(quality_control_func), 'quality_control_func must be a callable function' assert isinstance(window, (int, float)), 'window must be of type int or float' assert isinstance(key, (NoneType, str)), 'key must be None or of type string' assert isinstance(rebase, (NoneType, int, float)), 'rebase must be None or type int or float' assert isinstance(min_failures, int), 'min_failures must be type int' assert isinstance(error_message, (NoneType, str)), 'error_message must be None or of type string' df = self._setup_data(key) if df is None: return metadata = {} rebase_count = 0 history_window = datetime.timedelta(seconds=window) # The mask must be the same size as data # The streaming framework uses numpy arrays to improve performance but # still expects pandas DataFrames and Series in the user defined quality # control function to keep data types consitent on the user side. np_mask = pd.DataFrame(True, index=df.index, columns=df.columns).values np_data = df.values.astype('float64') ti = df.index.get_loc(df.index[0]+history_window) for i, t in enumerate(np.arange(ti,np_data.shape[0],1)): #t_start = df.index.get_loc(df.index[t]-history_window, method='nearest') t_start = df.index.get_indexer([df.index[t]-history_window], method='nearest')[0] t_timestamp = df.index[t] data_pt = pd.Series(np_data[t], index=df.columns) history = pd.DataFrame(np_data[t_start:t], index=range(t-t_start), columns=df.columns) mask_t, metadata[t_timestamp] = quality_control_func(data_pt, history) if i == 0: assert isinstance(mask_t, pd.Series), 'mask returned by quality_control_func must be of type pd.Series' assert isinstance(metadata[t_timestamp], pd.Series), 'metadata returned by quality_control_func must be of type pd.Series' np_mask[t] = mask_t.values np_data[~np_mask] = np.NAN # rebase if rebase is not None: data_history = np_data[t_start:t+1] # +1 so it includes history and current data point check_rebase = np.isnan(data_history).sum(axis=0)/data_history.shape[0] > rebase if sum(check_rebase) > 0: np_data[t][check_rebase] = df.iloc[t][check_rebase] rebase_count = rebase_count + sum(check_rebase) mask = pd.DataFrame(np_mask, index=df.index, columns=df.columns) self._append_test_results(mask, error_message, min_failures) # Convert metadata to a dataframe metadata = pd.DataFrame(metadata).T return metadata
### Functional approach
[docs] @_documented_by(PerformanceMonitoring.check_timestamp) def check_timestamp(data, frequency, expected_start_time=None, expected_end_time=None, min_failures=1, exact_times=True): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_timestamp(frequency, expected_start_time, expected_end_time, min_failures, exact_times) mask = pm.mask return {'cleaned_data': pm.data, 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_range) def check_range(data, bound, key=None, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_range(bound, key, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_increment) def check_increment(data, bound, key=None, increment=1, absolute_value=True, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_increment(bound, key, increment, absolute_value, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_delta) def check_delta(data, bound, window, key=None, direction=None, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_delta(bound, window, key, direction, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_outlier) def check_outlier(data, bound, window=None, key=None, absolute_value=False, streaming=False, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_outlier(bound, window, key, absolute_value, streaming, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_missing) def check_missing(data, key=None, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_missing(key, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_corrupt) def check_corrupt(data, corrupt_values, key=None, min_failures=1): pm = PerformanceMonitoring() pm.add_dataframe(data) pm.check_corrupt(corrupt_values, key, min_failures) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results}
[docs] @_documented_by(PerformanceMonitoring.check_custom_static, include_metadata=True) def check_custom_static(data, quality_control_func, key=None, min_failures=1, error_message=None): pm = PerformanceMonitoring() pm.add_dataframe(data) metadata = pm.check_custom_static(quality_control_func, key, min_failures, error_message) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results, 'metadata': metadata}
[docs] @_documented_by(PerformanceMonitoring.check_custom_streaming, include_metadata=True) def check_custom_streaming(data, quality_control_func, window, key=None, rebase=None, min_failures=1, error_message=None): pm = PerformanceMonitoring() pm.add_dataframe(data) metadata = pm.check_custom_streaming(quality_control_func, window, key, rebase, min_failures, error_message) mask = pm.mask return {'cleaned_data': data[mask], 'mask': mask, 'test_results': pm.test_results, 'metadata': metadata}