From ab803286cdfa0143f7faec4dd7eeb700afeec55f Mon Sep 17 00:00:00 2001 From: sayanc Date: Tue, 18 Jan 2022 16:34:23 -0800 Subject: [PATCH 01/11] fixes for batch models - exploration, validation, holidays --- luminaire/exploration/data_exploration.py | 19 +++++-- luminaire/model/lad_structural.py | 68 ++++++++++++++++++++++- luminaire/model/model_utils.py | 9 +++ 3 files changed, 91 insertions(+), 5 deletions(-) diff --git a/luminaire/exploration/data_exploration.py b/luminaire/exploration/data_exploration.py index 7597bd3..a03dcde 100644 --- a/luminaire/exploration/data_exploration.py +++ b/luminaire/exploration/data_exploration.py @@ -133,6 +133,9 @@ def add_missing_index(self, df=None, freq=None): import pandas as pd + # Adding a group by logic for duplicate index + df = df.groupby(df.index).mean() + # Create a new Pandas data frame based on the first valid index and # current date using the frequency defined by the use idx = pd.date_range(start=df.first_valid_index(), end=df.last_valid_index(), freq=freq) @@ -798,14 +801,19 @@ def kf_naive_outlier_detection(self, input_series, idx_position): from pykalman import KalmanFilter kf = KalmanFilter() - filtered_state_means, filtered_state_covariance = kf.em(input_series).filter(input_series) - residuals = input_series - filtered_state_means[:,0] + truncated_series = input_series[-(self.min_ts_length * 3):] \ + if idx_position == len(input_series) - 1 else input_series + idx_position = len(truncated_series) - 1 + + filtered_state_means, filtered_state_covariance = kf.em(truncated_series).filter(truncated_series) + + residuals = truncated_series - filtered_state_means[:, 0] is_anomaly = residuals[idx_position] < np.mean(residuals) \ - - (2.5 * np.sqrt(filtered_state_covariance)[idx_position][0][0]) \ + - (1 * np.sqrt(filtered_state_covariance)[idx_position][0][0]) \ or residuals[idx_position] > np.mean(residuals) \ - + (2.5 * np.sqrt(filtered_state_covariance)[idx_position][0][0]) + + (1 * np.sqrt(filtered_state_covariance)[idx_position][0][0]) return is_anomaly @@ -882,6 +890,9 @@ def _prepare(self, df, impute_only, streaming=False, **kwargs): df = df.iloc[-min(max_ts_length, len(df)):] df = self._truncate_by_data_gaps(df=df, target_metric=target_metric) + if len(df) < min_ts_length: + raise ValueError("Due to a recent data gap, training is waiting for more data to populate") + if not streaming and len(df) < min_ts_length: raise ValueError("The training data observed continuous missing data near the end. Require more stable " "data to train") diff --git a/luminaire/model/lad_structural.py b/luminaire/model/lad_structural.py index 9c817c9..386c0e4 100644 --- a/luminaire/model/lad_structural.py +++ b/luminaire/model/lad_structural.py @@ -442,12 +442,72 @@ def _training(self, data, ts_start, ts_end, min_ts_length=None, min_ts_mean=None return result, order - def train(self, data, optimize=False, **kwargs): + def _validate_model(self, data, hyper_params, result): + """ + This function validates a newly trained model before publishing + + :param pandas.DataFrame data: Input time series data + :param dict hyper_params: Hyperparameters for model training + :param dict result: Training outputs + :return: A validation flag for model to publish + + :rtype: bool + """ + + import numpy as np + import scipy.stats as st + + levene_alpha = 0.05 + grubb_alpha = 0.001 + f_test_alpha = 0.05 + mwu_alpha = 0.0005 + passed = True + + model = LADStructuralModel(hyper_params=self.hyper_params, **result) + + freq = "1" + result['freq'] if not any(char.isdigit() for char in result['freq']) else result['freq'] + + baseline = data[self._imputed_metric][-self.max_scoring_length:].values + baseline = (np.exp(baseline) - 1).tolist() if hyper_params['is_log_transformed'] else baseline.tolist() + + predictions = [] + pred_date = pd.Timestamp(result['training_end_date']) + for i in range(len(baseline)): + pred_date = pred_date + pd.Timedelta(freq) + result = model.score(baseline[i], pred_date) + if result['Success']: + predictions.append(result['Prediction']) + + if len(predictions) == len(baseline): + N = len(predictions) + + pvalue_levene = st.levene(predictions, baseline)[1] + pvalue_levene_diff = st.levene(np.diff(predictions), np.diff(baseline))[1] + pvalue_mwu = st.mannwhitneyu(predictions, baseline)[1] if predictions != baseline else 1.0 + + grubb_test_stat = max(abs(np.array(predictions) - np.mean(predictions))) / np.std(predictions, ddof=1) + t_cutoff = st.t.ppf((grubb_alpha / (2 * N)), df=N - 2) + grubb_test = grubb_test_stat > ((N - 1) / np.sqrt(N)) * np.sqrt((t_cutoff**2) / (N - 2 + (t_cutoff**2))) + + if grubb_test: + passed = False + if np.std(baseline, ddof=1) > 0: + if (not np.isnan(pvalue_levene) and not np.isnan(pvalue_levene_diff) and not np.isnan(pvalue_mwu)) and \ + (pvalue_levene < levene_alpha or pvalue_levene_diff < levene_alpha): + F = (np.std(predictions, ddof=1) ** 2) / (np.std(baseline, ddof=1) ** 2) + f_test_pval = 1 - st.f.cdf(F, N - 1, len(baseline) - 1) + if f_test_pval < f_test_alpha and pvalue_mwu < mwu_alpha: + passed = False + + return passed + + def train(self, data, optimize=False, validate=False, **kwargs): """ This function trains a structural LAD model for a given time series. :param pandas.DataFrame data: Input time series data :param bool optimize: Flag to identify whether called from hyperparameter optimization + :param bool validate: Flag to identify whether to run model validation after training :return: success flag, the model date and the trained lad structural model object :rtype: tuple[bool, str, LADStructuralModel object] @@ -493,6 +553,12 @@ def train(self, data, optimize=False, **kwargs): success = False if 'ErrorMessage' in result else True + if success and validate and not optimize: + passed = self._validate_model(data=data, hyper_params=self.hyper_params, result=result) + if not passed: + success = False + result['ErrorMessage'] = 'Model validation failed! Check any issues with the input data or the hyper-parameters.' + return success, kwargs['ts_end'], LADStructuralModel(hyper_params=self.hyper_params, **result) @classmethod diff --git a/luminaire/model/model_utils.py b/luminaire/model/model_utils.py index f826ada..0649b5f 100644 --- a/luminaire/model/model_utils.py +++ b/luminaire/model/model_utils.py @@ -58,6 +58,8 @@ class LADHolidays(pandas.tseries.holiday.AbstractHolidayCalendar): offset=_DateOffset(weekday=_H.SU(1))) Easter = _H.Holiday('Easter', month=1, day=1, offset=[_H.Easter(), _H.Day(0)]) + USThanksgivingWednesday = _H.Holiday('Thanksgiving Wednesday', month=11, day=1, + offset=[_DateOffset(weekday=_H.TH(4)), _DateOffset(days=-1)]) USThanksgivingDay = _H.Holiday('Thanksgiving', month=11, day=1, offset=_DateOffset(weekday=_H.TH(4))) USThanksgivingFriday = _H.Holiday('Thanksgiving Friday', month=11, day=1, @@ -67,6 +69,7 @@ class LADHolidays(pandas.tseries.holiday.AbstractHolidayCalendar): USThanksgivingSunday = _H.Holiday('Thanksgiving Sunday', month=11, day=1, offset=[_DateOffset(weekday=_H.TH(4)), _DateOffset(days=3)]) + Dec23 = _H.Holiday('December 23', month=12, day=23) ChristmasEve = _H.Holiday('Christmas Eve', month=12, day=24) ChristmasDay = _H.Holiday('Christmas Day', month=12, day=25) ChristmasDayObserved = _H.Holiday('Christmas Day Observed', month=12, day=25, observance=_H.nearest_workday) @@ -79,6 +82,8 @@ class LADHolidays(pandas.tseries.holiday.AbstractHolidayCalendar): NewYearsDay = _H.Holiday('New Years Day', month=1, day=1) NewYearsDayObserved = _H.Holiday('New Years Day Observed', month=1, day=1, observance=_H.nearest_workday) + Jan2 = _H.Holiday('January 2', month=1, day=2) + Jan3 = _H.Holiday('January 3', month=1, day=3) rules = [ USMemorialDay, @@ -105,10 +110,12 @@ class LADHolidays(pandas.tseries.holiday.AbstractHolidayCalendar): Superbowl, Easter, + USThanksgivingWednesday, USThanksgivingDay, USThanksgivingFriday, USThanksgivingSaturday, USThanksgivingSunday, + Dec23, ChristmasEve, ChristmasDay, ChristmasDayObserved, @@ -120,6 +127,8 @@ class LADHolidays(pandas.tseries.holiday.AbstractHolidayCalendar): Dec31, NewYearsDay, NewYearsDayObserved, + Jan2, + Jan3, ] def __init__(self, name=None, holiday_rules=None): From 7232ef0c957ef8e98c106286d5910084189510db Mon Sep 17 00:00:00 2001 From: sayanc Date: Fri, 21 Jan 2022 23:02:00 -0800 Subject: [PATCH 02/11] statsmodel and scipy package upgrade tested, models updated for unit test --- luminaire/model/lad_structural.py | 16 +++++++--------- luminaire/tests/models/lad_structural_model | Bin 536204 -> 4202789 bytes .../models/lad_structural_model_log_seasonal | Bin 525263 -> 5215953 bytes luminaire/tests/models/window_density_model | Bin 1514732 -> 4688823 bytes requirements.txt | 4 ++-- 5 files changed, 9 insertions(+), 11 deletions(-) diff --git a/luminaire/model/lad_structural.py b/luminaire/model/lad_structural.py index 386c0e4..913a6c0 100644 --- a/luminaire/model/lad_structural.py +++ b/luminaire/model/lad_structural.py @@ -237,7 +237,7 @@ def _seasonal_arima(self, endog=None, exog=None, p=None, d=None, q=None, imodels """ import numpy as np - import statsmodels.tsa.arima_model as arima + import statsmodels.tsa.arima.model as arima # Extract the exogenous variable generated based on (imodels * 2) number of most significant # frequencies @@ -257,13 +257,7 @@ def _seasonal_arima(self, endog=None, exog=None, p=None, d=None, q=None, imodels try: stepwise_fit.append(arima.ARIMA(endog=endog, exog=exog, - order=(p, d, q)).fit(seasonal=False, trace=False, - method='css', - solver='bfgs', - error_action='ignore', - stepwise_fit=True, - warn_convergence=False, - disp=False)) + order=(p, d, q)).fit(method='statespace')) except Exception as e: raise LADStructuralError(message=str(e)) @@ -635,7 +629,11 @@ def _predict(cls, model, is_log_transformed, else: pred_exog['fourier_feature'] = seasonal_feature_scoring[:forecast_ndays] - forecast = list(model.forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog)) + forecast = [] + forecast_obj = model.get_forecast(steps=forecast_ndays, alpha=alpha, exog=pred_exog) + forecast.append(np.real(forecast_obj.predicted_mean).tolist()) # adding prediction mean to forecast + forecast.append(np.real(forecast_obj.se_mean).tolist()) # adding prediction standard error to forecast + forecast.append(np.real(forecast_obj.conf_int()).tolist()) # adding predictions ci's to forecast interpolated_training_data = list(zip(*training_tail))[1] for order in list(reversed(range(order_of_diff))): diff --git a/luminaire/tests/models/lad_structural_model b/luminaire/tests/models/lad_structural_model index 4297dcbd7f9175ba317276ac94ef5f12aa789d23..ab8cb31b6155537cf0d1388ee6a64c6ae165f056 100644 GIT binary patch literal 4202789 zcmeF&2YeLO+Q;z;A@tt6bV3UtL6BmWA|RoIh=_(wvdJW*v71mu0@4CY5f()hM66gr zKtRx}1w=(brAiS+Kv5}z6a{_HNq7|IO)f|Y0e!n}nG}|o*OOj5Wb#Eg%L3bzk)g*b-BM|vi@eG)vuer}&Zo?u^h 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