How to use raw_update method in pypom_form

Best Python code snippet using pypom_form_python

Monitor.py

Source:Monitor.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3"""4Created on Fri Jun 22 10:52:06 20185@author: zhangzhanming6"""7import pandas as pd8import time9import threading10import datetime11class AccDataBase(object):12    feature_columns = ['START_TIME', 'STOP_TIME', 'MEAN_VM', 'STD_VM', 'MAX_VM', 'DOM_FREQ_VM',13       'DOM_FREQ_POWER_RATIO_VM', 'HIGHEND_FREQ_POWER_RATIO_VM', 'RANGE_VM',14       'ACTIVE_SAMPLE_PERC_VM', 'NUMBER_OF_ACTIVATIONS_VM',15       'ACTIVATION_INTERVAL_VAR_VM', 'MEDIAN_X_ANGLE', 'MEDIAN_Y_ANGLE',16       'MEDIAN_Z_ANGLE', 'RANGE_X_ANGLE', 'RANGE_Y_ANGLE', 'RANGE_Z_ANGLE']17    18    #initialized feature data with column names19    def __init__(self, monitor=None):20        self.monitor = monitor21        self.featuredata = pd.DataFrame(columns = self.feature_columns)22        self.annotationdata = pd.DataFrame()23        self.rawdata = pd.DataFrame()24    def set_data_set(self, featuredata=None, annotationdata=None, rawdata=None):25        if isinstance(featuredata, str):26            self.featuredata = pd.read_csv(featuredata)27        if isinstance(annotationdata, str):28            self.annotationdata = pd.read_csv(annotationdata)29        if isinstance(rawdata, str):30            self.rawdata = pd.read_csv(rawdata)31        if isinstance(featuredata, pd.DataFrame):32            self.featuredata = featuredata33        if isinstance(annotationdata, pd.DataFrame):34            self.annotationdata = annotationdata35        if isinstance(rawdata, pd.DataFrame):36            self.rawdata = rawdata37    def set_monitor(self, monitor):38        self.monitor = monitor39    def update(self, feature_update=None, annotation_update=None,40               raw_update=None):41        self.__append_data(feature_update, annotation_update, raw_update)42# =============================================================================43#         self.__delete_data(feature_update is not None,44#                            annotation_update is not None,45#                            raw_update is not None)46# =============================================================================47    def __append_data(self, feature_update, annotation_update, raw_update):48        if feature_update is not None:49            if self.featuredata.shape[0] == 0:50                self.featuredata = feature_update51            else: 52                self.featuredata = pd.concat([self.featuredata, feature_update]).reset_index(drop=True)53        if annotation_update is not None:54            if self.annotationdata.shape[0] == 0:55                self.annotationdata = annotation_update56            else:57                self.annotationdata = pd.concat([self.annotationdata, annotation_update]).reset_index(drop=True)58        if raw_update is not None:59            if self.rawdata.shape[0] == 0:60                self.rawdata = raw_update61            else:62                self.rawdata = pd.concat([self.rawdata, raw_update]).reset_index(drop=True)63    def __delete_data(self, update_feature, update_annotation, update_raw):64        if update_feature:65            if len(self.featuredata.index):66                pass67class RepeatedTimer(object):68    def __init__(self, interval, function, *args, **kwargs):69        self.count = 070        self._timer = None71        self.interval = interval72        self.function = function73        self.args = args74        self.kwargs = kwargs75        self.is_running = False76        self.next_call = time.time()77        self.start()78    def _run(self):79        self.is_running = False80        self.start()81        self.function(self.count, *self.args, **self.kwargs)82        self.count += 183    def start(self):84        if not self.is_running:85            self.next_call += self.interval86            self._timer = threading.Timer(self.next_call - time.time(), self._run)87        self._timer.start()88        self.is_running = True89    def stop(self):90        self._timer.cancel()91        self.is_running = False92class Monitor(object):93    def __init__(self, refresh=12.5, sampling_rate=80, feature_time = 12.8,test=True):94        self.__observers = []95        self.feature_time = feature_time96        self.refresh = refresh97        self.sampling_rate = sampling_rate98        self.test = test99    def register_observer(self, observer):100        self.__observers.append(observer)101    def notify_observers(self, feature_update=None, annotation_update=None,102               raw_update=None):103        for observer in self.__observers:104            observer.update(feature_update=feature_update,105                            annotation_update=annotation_update,106                            raw_update=raw_update)107    def listen(self):108        if self.test:109            featuredata = pd.read_csv('/Users/zhangzhanming/Desktop/mHealth/Data/SPADES_2/Derived/Preprocessed/2015/10/08/14/ActigraphGT9X-PostureAndActivity-NA.TAS1E23150066-PostureAndActivity.2015-10-08-14-00-00-000-M0400.feature.csv')110            featuredata = featuredata.values.tolist()111            featuredata.sort(key=lambda x: x[1])112            #raw_annotation = pd.read_csv('/Users/zhangzhanming/Desktop/mHealth/Data/SPADES_2/MasterSynced/2015/10/08/14/splitted.annotation.csv')113            #classmapping = pd.read_csv('/Users/zhangzhanming/Desktop/mHealth/Data/SPADES_2/MasterSynced/2015/10/08/14/class_mapping.csv')114            #annotationdata = pd.merge(raw_annotation, classmapping, left_on='LABEL_NAME', right_on='activity', how='inner')115            #annotationdata = annotationdata.drop('activity', axis=1)116            annotationdata = pd.read_csv('/Users/zhangzhanming/Desktop/mHealth/Data/SPADES_2/Derived/Preprocessed/2015/10/08/14/SPADESInLab.alvin-SPADESInLab.2015-10-08-14-10-41-252-M0400.class.csv')117            annotationdata = annotationdata.values.tolist()118            # sort annotation data by end time, just like in real situations119            annotationdata.sort(key=lambda x: x[1])120            rawdata = pd.read_csv('/Users/zhangzhanming/Desktop/mHealth/Data/SPADES_2/Derived/Preprocessed/2015/10/08/14/ActigraphGT9X-AccelerationCalibrated-NA.TAS1E23150066-AccelerationCalibrated.2015-10-08-14-00-00-000-M0400.sensor.csv')121# =============================================================================122#             last_time = pd.to_datetime(rawdata.iloc[0,0])123#             feature_interval = datetime.timedelta(seconds=self.feature_time)124#             threshold_time = last_time+feature_interval125#             feature_count = 0126# 127# =============================================================================128            def __get_new_data(count, refresh, sampling_rate, rawdata):129                feature_update = []130                raw_update = rawdata[int(count*refresh/1000*sampling_rate):131                    int((count+1)*refresh/1000*sampling_rate)]132                currtime = pd.to_datetime(raw_update.iloc[-1,0])133# =============================================================================134#                 nonlocal threshold_time135#                 if  currtime > threshold_time:136#                     nonlocal feature_count137#                     feature_update = featuredata.iloc[feature_count:feature_count+1,:]138#                     threshold_time = currtime + feature_interval139#                     feature_count += 1140# =============================================================================141                annotation_update = []142                while currtime >= pd.to_datetime(annotationdata[0][1]):143                    annotation_update.append(annotationdata.pop(0))144                    feature_update.append(featuredata.pop(0))145                    146                feature_update=pd.DataFrame(feature_update, 147                                               columns=['START_TIME', 'STOP_TIME', 'MEAN_VM', 'STD_VM', 'MAX_VM', 'DOM_FREQ_VM',148       'DOM_FREQ_POWER_RATIO_VM', 'HIGHEND_FREQ_POWER_RATIO_VM', 'RANGE_VM',149       'ACTIVE_SAMPLE_PERC_VM', 'NUMBER_OF_ACTIVATIONS_VM',150       'ACTIVATION_INTERVAL_VAR_VM', 'MEDIAN_X_ANGLE', 'MEDIAN_Y_ANGLE',151       'MEDIAN_Z_ANGLE', 'RANGE_X_ANGLE', 'RANGE_Y_ANGLE', 'RANGE_Z_ANGLE'])152                annotation_update=pd.DataFrame(annotation_update, 153                                               columns=['START_TIME', 'STOP_TIME', 'posture', 'four_classes', 'MDCAS',154       'indoor_outdoor', 'activity', 'activity_intensity', 'hand_gesture'])155                self.notify_observers(raw_update = raw_update, 156                                      feature_update = feature_update,157                                      annotation_update = annotation_update)158            self.rt = RepeatedTimer(self.refresh/1000, __get_new_data, self.refresh,159                              self.sampling_rate, rawdata)160# =============================================================================161#             try:162#                 time.sleep(64)163#             finally:164#                 self.rt.stop()165# 166# =============================================================================167if __name__ == '__main__':168    monitor = Monitor()169    db = AccDataBase(monitor)170    monitor.register_observer(db)...

contrib.py

Source:contrib.py

...8        }9        update_query = {10            '$set': {field: field_value}11        }12        self.raw_update(filter_query, update_query)13    def add(self, field, object_pk, field_value, cnt_field=None):14        filter_query = {15            '_id': ObjectId(object_pk),16            field: {'$ne': field_value}17        }18        update_query = {19            '$addToSet': {field: field_value}20        }21        if cnt_field:22            update_query['$inc'] = {cnt_field: 1}23        self.raw_update(filter_query, update_query)24    def batch_add(self, field, object_pks, field_value, cnt_field=None):25        filter_query = {26            '_id': {'$in': map(lambda pk: ObjectId(pk), object_pks)},27            field: {'$ne': field_value}28        }29        update_query = {30            '$addToSet': {field: field_value}31        }32        if cnt_field:33            update_query['$inc'] = {cnt_field: 1}34        self.raw_update(filter_query, update_query)35    def remove(self, field, object_pk, field_value, cnt_field=None):36        filter_query = {37            '_id': ObjectId(object_pk),38            field: field_value39        }40        update_query = {41            '$pull': {field: field_value}42        }43        if cnt_field:44            update_query['$inc'] = {cnt_field: -1}45        self.raw_update(filter_query, update_query)46    def find_and_modify(self, collection_name, query={}, update=None, upsert=False, **kwargs):47        database_wrapper = connections['default']48        collection = database_wrapper.get_collection(collection_name)49        result = collection.find_and_modify(query, update=update, upsert=upsert, **kwargs)50        return result51    def pymongo_aggregate(self, collection_name, match=None, group=None, project=None, unwind=None, sort=None, extra=None):52        database_wrapper = connections['default']53        collection = database_wrapper.get_collection(collection_name)54        query = []55        if match:56            query.append({"$match": match})57        if project:58            query.append({"$project": project})59        if unwind:60            query.append({"$unwind": unwind})61        if group:62            query.append({"$group": group})63        if sort:64            query.append({"$sort": sort})65        if extra:66            query.append(extra)67        r = collection.aggregate(query)68        if r["ok"] == 1:69            return r["result"]70        else:71            return []72    def unset(self, field, object_pk):73        filter_query = {74            '_id': ObjectId(object_pk),75        }76        update_query = {77            '$unset': {field: 1}78        }79        self.raw_update(filter_query, update_query)        80    def batch_remove(self, field, object_pks, field_value, cnt_field=None):81        filter_query = {82            '_id': {'$in': map(lambda pk: ObjectId(pk), object_pks)},83            field: field_value84        }85        update_query = {86            '$pull': {field: field_value}87        }88        if cnt_field:89            update_query['$inc'] = {cnt_field: -1}90        self.raw_update(filter_query, update_query)91    def get_by_items(self, field, items):92        filter_query = {93            field: {94                '$all': items,95                '$size': len(items)96            }97        }98        return self.raw_query(filter_query)99    def clean_multiple_objects(self, **query):100        objs = self.filter(**query)101        obj = objs[0]102        for each_obj in objs[1:]:103            each_obj.delete()104        return obj...

gradient_descent.py

Source:gradient_descent.py

1import logging2import numpy as np3logger = logging.getLogger(__name__)4class GradientDescent(object):5    def __init__(self, learning_rate, inertia=0.0, annealing=np.Inf, last_learning_rate=None):6        self.last_learning_rate = last_learning_rate7        self.learning_rate = learning_rate8        self.inertia = inertia9        self.annealing = annealing10        self.last_update = None11        self.n_iter = 012    def transform(self, grad):13        """Individuals must be on the LAST dimensions"""14        p_anneal = self.n_iter / self.annealing15        if self.last_learning_rate is None:16            learning_rate = self.learning_rate / (1.0 + p_anneal)17        else:18            g = (self.last_learning_rate / self.annealing) ** min(p_anneal, 1.0)19            learning_rate = self.learning_rate * g20        self.n_iter += 1.021        raw_update = learning_rate * grad.mean(axis=-1)22        if self.last_update is None:23            self.last_update = raw_update24        self.last_update = (self.inertia * self.last_update +25                            (1 - self.inertia) * raw_update)26        return self.last_update27    def __str__(self):28        return ("<GradientDescent"29                " learning_rate={learning_rate}"30                " last_learning_rate={last_learning_rate}"31                " inertia={inertia}"...

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