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How to use ready_func method in Airtest

Best Python code snippet using Airtest

server_app.py

Source:server_app.py

...67        self.old_aggregate = old_aggregate68        self.old_data = old_data69        self.memory = memory70    def initialize(self):71        self.ready_func(False, agg=self.old_aggregate, data=self.old_data, memory=self.memory)72    def aggregate_univariate(self, client_data):73        if self.project.internal_state == "init":74            logger.info(f'[{self.project.id}] Univariate aggregation')75            results, aggregation, n_samples = univariate_analysis(client_data, int(self.project.privacy_level))76            prepare_univarate_results(results, aggregation, self.project)77            sleep(1)78            self.project.internal_state = "finished"79            self.ready_func(True, {}, client_data, self.memory)80            self.project.sample_number = n_samples81            self.project.state = "finished"82            cleanup(self.project)83            self.project.run_end = timezone.now()84    def smpc_aggregate_univariate(self, client_data):85        if self.project.internal_state == 'init':86            send_to_local = client_data87            self.project.internal_state = "local_calc"88            self.ready_func(False, send_to_local, client_data, self.memory)89        elif self.project.internal_state == 'local_calc':90            data_to_broadcast = distribute_smpc(client_data)91            send_to_local = data_to_broadcast92            self.project.internal_state = "smpc_agg"93            self.ready_func(False, send_to_local, client_data, self.memory)94        elif self.project.internal_state == 'smpc_agg':95            data = list(client_data.values())96            global_agg = aggregate_smpc(data, exp=0)97            dfs = {}98            global_agg = global_agg["local_results"]99            for key in global_agg:100                df = pd.DataFrame.from_dict(global_agg[key])101                df.index = df.index102                dfs[key] = df103            results, aggregation_dfs = univariate_analysis(dfs, privacy_level=self.project.privacy_level, smpc=True)104            n_samples = 0105            for key in aggregation_dfs:106                n_samples = n_samples + aggregation_dfs[key].iloc[0, 1]107            prepare_univarate_results(results, aggregation_dfs, self.project)108            sleep(1)109            self.project.internal_state = "finished"110            self.ready_func(True, {"state": "finished"}, None)111            self.project.sample_number = n_samples112            self.project.state = "finished"113            cleanup(self.project)114            self.project.run_end = timezone.now()115    def aggregate_cox(self, client_data):116        if self.project.internal_state == "init":117            logger.info(f'[{self.project.id}] Compute global mean')118            norm_mean = coxph.compute_global_mean(client_data)119            self.project.internal_state = "norm_std"120            send_to_local = {"norm_mean": norm_mean}121            self.memory = send_to_local.copy()122            self.ready_func(False, send_to_local, client_data, self.memory)123        elif self.project.internal_state == "norm_std":124            logger.info(f'[{self.project.id}] Compute global std')125            norm_std = coxph.compute_global_std(client_data)126            self.project.internal_state = "local_init"127            send_to_local = {"norm_std": norm_std}128            self.memory.update(send_to_local)129            self.ready_func(False, send_to_local, client_data, self.memory)130        elif self.project.internal_state == "local_init":131            logger.info(f'[{self.project.id}] Initialization')132            D, zr, count_d, n_samples = coxph.global_initialization(client_data)133            covariates = zr.axes[0]134            beta = np.zeros((len(covariates),))135            step_sizer = StepSizer(None)136            step_size = step_sizer.next()137            delta = np.zeros_like(beta)138            iteration = 0139            converging = True140            self.memory.update({"beta": beta, "iteration": iteration, "D": D, "zr": zr,141                                "count_d": count_d, "n_samples": n_samples, "step_sizer": step_sizer,142                                "step_size": step_size, "delta": delta, "converging": converging})143            send_to_local = {"beta": beta, "iteration": iteration}144            self.project.internal_state = "iteration_update"145            self.ready_func(False, send_to_local, client_data, self.memory)146        elif self.project.internal_state == "iteration_update":147            iteration = self.memory["iteration"]148            logger.info(f'[{self.project.id}] Iteration update {str(iteration)}')149            beta = self.memory["beta"]150            zr = self.memory["zr"]151            step_sizer = self.memory["step_sizer"]152            step_size = self.memory["step_size"]153            iteration = self.memory["iteration"]154            n_samples = self.memory["n_samples"]155            count_d = self.memory["count_d"]156            D = self.memory["D"]157            converging = self.memory["converging"]158            new_beta, converging, hessian, step_size, iteration, delta = coxph.iteration_update(159                client_data, beta, zr, converging, step_sizer, step_size, iteration,160                n_samples, count_d, D, penalization=self.project.penalizer,161                l1_ratio=self.project.l1_ratio, max_steps=self.project.max_iters)162            if converging:163                send_to_local = {"beta": new_beta, "iteration": iteration}164                self.memory.update(send_to_local)165                self.project.internal_state = "iteration_update"166                self.ready_func(False, send_to_local, client_data, self.memory)167            else:168                logger.info(f'[{self.project.id}] Stopping criterion fulfilled')169                norm_std = self.memory["norm_std"]170                summary_df, params, standard_errors = coxph.create_summary(norm_std, new_beta, zr, hessian)171                send_to_local = {"summary_df": summary_df, "params": params, "standard_errors": standard_errors,172                                 "beta": new_beta}173                self.memory.update(send_to_local)174                self.project.internal_state = "c-index"175                self.ready_func(False, send_to_local, client_data, self.memory)176        elif self.project.internal_state == "c-index":177            logger.info(f'[{self.project.id}] Finishing...')178            global_c, sample_number = coxph.calculate_concordance_index(client_data)179            prepare_cox_results(self.memory["summary_df"], self.memory["params"],180                                self.memory["standard_errors"], global_c, sample_number, self.project)181            sleep(1)182            self.project.internal_state = "finished"183            self.ready_func(True, {"state": "finished"}, None, self.memory)184            self.project.state = "finished"185            logger.info(f'[{self.project.id}] Computation finished')186            cleanup(self.project)187            self.project.run_end = timezone.now()188    def smpc_aggregate_cox(self, client_data):189        exp = 10190        if self.project.internal_state == 'init':191            send_to_local = client_data192            self.project.internal_state = "norm_mean"193            self.ready_func(False, send_to_local, client_data, self.memory)194        elif self.project.internal_state == "norm_mean":195            data_to_broadcast = distribute_smpc(client_data)196            send_to_local = data_to_broadcast197            self.project.internal_state = "smpc_agg_norm_mean"198            self.ready_func(False, send_to_local, client_data, self.memory)199        elif self.project.internal_state == "smpc_agg_norm_mean":200            data = list(client_data.values())201            global_agg = aggregate_smpc(data, exp)202            n_samples = global_agg["n_samples"] * 10**exp203            self.memory = {"n_samples": n_samples}204            global_mean = pd.Series(global_agg["mean"]) / n_samples205            send_to_local = global_mean206            self.project.internal_state = "norm_std"207            self.ready_func(False, send_to_local, client_data, self.memory)208        elif self.project.internal_state == "norm_std":209            data_to_broadcast = distribute_smpc(client_data)210            send_to_local = data_to_broadcast211            self.project.internal_state = "smpc_agg_norm_std"212            self.ready_func(False, send_to_local, client_data, self.memory)213        elif self.project.internal_state == "smpc_agg_norm_std":214            data = list(client_data.values())215            global_agg = aggregate_smpc(data, exp)216            n_samples = self.memory["n_samples"]217            norm_std = np.sqrt(pd.Series(global_agg["std"]) / (n_samples - 1))218            self.memory.update({"norm_std": norm_std})219            send_to_local = norm_std220            self.project.internal_state = "local_init"221            self.ready_func(False, send_to_local, client_data, self.memory)222        elif self.project.internal_state == "local_init":223            data_to_broadcast = distribute_smpc(client_data)224            send_to_local = data_to_broadcast225            self.project.internal_state = "smpc_agg_local_init"226            self.ready_func(False, send_to_local, client_data, self.memory)227        elif self.project.internal_state == "smpc_agg_local_init":228            data = list(client_data.values())229            global_agg = aggregate_smpc(data, exp)230            global_zlr = pd.Series(global_agg["zlr"])231            covariates = pd.Series(global_zlr).index.tolist()232            beta = np.zeros((len(covariates),))233            step_sizer = StepSizer(None)234            step_size = step_sizer.next()235            delta = np.zeros_like(beta)236            beta += step_size * delta237            global_count_d = pd.Series(global_agg["numb_d_set"])238            global_count_d.index = global_count_d.index.astype(float)239            global_count_d.index = global_count_d.index.astype(str)240            iteration = 0241            converging = True242            global_distinct_times = np.arange(self.project.from_time, self.project.to_time,243                                              self.project.step_size).tolist()244            global_distinct_times.reverse()245            self.memory.update(246                {"global_zlr": global_zlr, "covariates": covariates, "beta": beta, "step_sizer": step_sizer,247                 "step_size": step_size, "global_count_d": global_count_d, "iteration": iteration,248                 "converging": converging, "global_distinct_times": global_distinct_times})249            send_to_local = [beta, iteration]250            self.project.internal_state = "iteration_update"251            self.ready_func(False, send_to_local, data, self.memory)252        elif self.project.internal_state == "iteration_update":253            data_to_broadcast = distribute_smpc(client_data)254            send_to_local = data_to_broadcast255            self.project.internal_state = "smpc_agg_update"256            self.ready_func(False, send_to_local, client_data, self.memory)257        elif self.project.internal_state == "smpc_agg_update":258            data = list(client_data.values())259            global_agg = aggregate_smpc(data, exp)260            iteration = self.memory["iteration"]261            logger.info(f'[{self.project.id}] Iteration update {str(iteration)}')262            beta = self.memory["beta"]263            zr = self.memory["global_zlr"]264            step_sizer = self.memory["step_sizer"]265            step_size = self.memory["step_size"]266            iteration = self.memory["iteration"]267            n_samples = self.memory["n_samples"]268            count_d = self.memory["global_count_d"]269            D = self.memory["global_distinct_times"]270            converging = self.memory["converging"]271            new_beta, converging, hessian, step_size, iteration, delta = coxph.iteration_update(272                global_agg, beta, zr, converging, step_sizer, step_size, iteration,273                n_samples, count_d, D, penalization=self.project.penalizer,274                l1_ratio=self.project.l1_ratio, max_steps=self.project.max_iters, smpc=self.project.smpc)275            if converging:276                send_to_local = [new_beta, iteration]277                self.memory.update({"beta": new_beta, "iteration": iteration})278                self.project.internal_state = "iteration_update"279                self.ready_func(False, send_to_local, client_data, self.memory)280            else:281                logger.info(f'[{self.project.id}] Stopping criterion fulfilled')282                norm_std = self.memory["norm_std"]283                summary_df, params, standard_errors = coxph.create_summary(norm_std, new_beta, zr, hessian)284                send_to_local = params285                self.memory.update({"summary_df": summary_df, "params": params, "standard_errors": standard_errors})286                self.project.internal_state = "c_index"287                self.ready_func(False, send_to_local, client_data, self.memory)288        elif self.project.internal_state == "c_index":289            data_to_broadcast = distribute_smpc(client_data)290            send_to_local = data_to_broadcast291            self.project.internal_state = "smpc_agg_c_index"292            self.ready_func(False, send_to_local, client_data, self.memory)293        elif self.project.internal_state == "smpc_agg_c_index":294            data = list(client_data.values())295            global_agg = aggregate_smpc(data, 0)296            n_samples = self.memory["n_samples"]297            global_c = (global_agg["c-index"] / 10) / n_samples298            summary_df = self.memory["summary_df"]299            params = self.memory["params"]300            standard_errors = self.memory["standard_errors"]301            prepare_cox_results(summary_df, params, standard_errors, global_c, n_samples, self.project)302            self.project.internal_state = "finished"303            self.ready_func(True, {"finished": True}, client_data, self.memory)304            logger.info(f'[{self.project.id}] Computation finished')305            self.project.run_end = timezone.now()306            delete_blob(f'p{self.project.id}_memory')307            sleep(2)308            cleanup(self.project)309            self.project.state = "finished"310    def aggregate(self, client_data):311        try:312            self.check_error(client_data)313            logger.info(f'[{self.project.id}] Aggregate')314            logger.info(f'[{self.project.id}] {self.project.internal_state}')315            if self.project.method == "univariate":316                if not self.project.smpc:317                    self.aggregate_univariate(client_data)318                else:319                    self.smpc_aggregate_univariate(client_data)320            elif self.project.method == "cox":321                if not self.project.smpc:322                    self.aggregate_cox(client_data)323                else:324                    self.smpc_aggregate_cox(client_data)325            self.project.save()326        except Exception as e:327            self.project.state = "error"328            self.project.error_message = f'Error: {e}'329            self.project.run_end = timezone.now()330            self.project.save()331            logger.warning(f'[{self.project.id}] Computation finished with an error: {e}')332            send_to_local = {"error": self.project.error_message}333            self.ready_func(True, send_to_local, client_data, self.memory)334            delete_blob(f'p{self.project.id}_memory')335            cleanup(self.project)336            raise337    def check_error(self, client_data):338        for client_id in client_data.keys():339            client = client_data[client_id]340            try:341                if "error" in client.keys():342                    self.project.state = "error"343                    self.project.error_message = client["error"]344                    self.project.run_end = timezone.now()345                    self.project.save()346                    logger.warning(f'[{self.project.id}] Computation finished with a client side error.')347                    self.ready_func(True, {"error": self.project.error_message}, client_data, self.memory)348                    delete_blob(f'p{self.project.id}_memory')349                    cleanup(self.project)350                    return351            except AttributeError:...

main.py

Source:main.py

...153                for item in iter(self.readq.get, None):154                    if self.state.machine.interrupted.is_set():155                        logger.info(f"{self.name} interrupted")156                        break157                    result = self.state.ready_func(item)158                    self.coordinator.add_task(Machine.Task(item, self.state.id, result))159                else:...

server_funcs.py

Source:server_funcs.py

...5from fed_algo.storage import delete_blob, load_blob, dump_blob, cleanup6from fed_algo.transformations import deserialize_bytes, serialize_bytes7logger = logging.getLogger(__name__)8def initialize(project):9    def ready_func(ready, agg=None, data=None, memory=None):10        dump_blob(f'p{project.id}_aggregate', serialize_bytes(agg))11        dump_blob(f'p{project.id}_data', serialize_bytes(data))12        dump_blob(f'p{project.id}_memory', serialize_bytes(memory))13        project.state = 'waiting' if not ready else 'finished'14        project.step += 115        project.save()16    logger.info(f'[{project.id}]Initialize server for aggregation')17    ServerApp(project=project, ready_func=ready_func, old_aggregate=None, old_data=None, memory=None).initialize()18def aggregate(proj: Project, lock: FileLock):19    with lock:20        logger.info(f'[{proj.id}] Aggregate local client data')21        client_data = {}22        for mem in proj.members.all():23            logger.debug(f'[{proj.id}] Add local client data to aggregation: {str(mem.id)}')24            client_bytes = load_blob(f'c{mem.id}_data')25            deserialized = deserialize_bytes(client_bytes)26            client_data[mem.id] = deserialized27            delete_blob(f'c{mem.id}_data')28        def ready_func(ready, agg=None, data=None, memory=None):29            dump_blob(f'p{proj.id}_aggregate', serialize_bytes(agg))30            if "smpc" in proj.internal_state:31                for mem in proj.members.all():32                    dump_blob(f'p{proj.id}_{mem.id}', serialize_bytes(agg[mem.id]))33            dump_blob(f'p{proj.id}_data', serialize_bytes(data))34            dump_blob(f'p{proj.id}_memory', serialize_bytes(memory))35            if not ready:36                proj.state = 'waiting'37            else:38                cleanup(proj)39            proj.step += 140            proj.save()41        proj.state = 'running'42        proj.save()...

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