From e4dda7eef947158fd2c69a76277208879446dfb5 Mon Sep 17 00:00:00 2001 From: Tianyu Liu Date: Wed, 19 Jun 2024 12:34:58 +0000 Subject: [PATCH 01/35] vllm inference --- benchmark/benchmark_vllm_inference.py | 227 ++++++++++++++++++++++++++ 1 file changed, 227 insertions(+) create mode 100644 benchmark/benchmark_vllm_inference.py diff --git a/benchmark/benchmark_vllm_inference.py b/benchmark/benchmark_vllm_inference.py new file mode 100644 index 00000000..038835b7 --- /dev/null +++ b/benchmark/benchmark_vllm_inference.py @@ -0,0 +1,227 @@ +import pickle +from argparse import ArgumentParser +from random import seed + +import numpy as np +from arsenal import colors +from hfppl import CachedCausalLM +from torch import manual_seed +from transformers import AutoTokenizer, set_seed + +from genparse import Float +from genparse.cfglm import EarleyBoolMaskCFGLM +from genparse.lm import AsyncGreedilyTokenizedLLM +from genparse.proposal import CharacterProposal, TokenProposal +from genparse.steer import HFPPLSampler +from genparse.util import LarkStuff + +import vllm + +p = ArgumentParser() +p.add_argument('--model', choices=['gpt2', 'codellama'], required=True) +p.add_argument('--proposal', choices=['token', 'character'], default='character') +p.add_argument('--particles', type=int, default=1) +p.add_argument('--reps', type=int, default=1) +p.add_argument('--max-tokens', type=int, default=100) +p.add_argument('--verbosity', type=int, default=0) +p.add_argument('--seed', type=int, default=0) +p.add_argument( + '--inference', + choices=['smc-standard', 'smc-steer', 'importance-sampling'], + default='smc-standard', +) +args = p.parse_args() + + +RANDOM_SEED = args.seed +set_seed(RANDOM_SEED) +seed(RANDOM_SEED) +manual_seed(RANDOM_SEED) + + +# TODO: +# replace hfppl_llm with vllm +# need to implement p_next / next_token_logprobs for vllm + +# class vllmppl_llm(vllm): +# ... +# def next_token_logprobs(xs): +# ... + +class vllmppl_llm(vllm.VLLM): + + def next_token_logprobs(self, xs): + # call the vllm engine of VLLM + step_outputs = self.llm_engine.step() + # TODO: + # Check what's in step_outputs + for output in step_outputs: + if output.finished: + outputs.append(output) + if use_tqdm: + if isinstance(output, RequestOutput): + # Calculate tokens only for RequestOutput + total_in_toks += len(output.prompt_token_ids) + in_spd = total_in_toks / pbar.format_dict["elapsed"] + total_out_toks += sum( + len(stp.token_ids) for stp in output.outputs) + out_spd = total_out_toks / pbar.format_dict[ + "elapsed"] + pbar.postfix = ( + f"est. speed input: {in_spd:.2f} toks/s, " + f"output: {out_spd:.2f} toks/s") + pbar.update(1) + return self.model(xs) + +if args.model == 'gpt2': + import transformers + + from genparse.lm import LLM + + MODEL_ID = 'gpt2' + hfppl_llm = LLM(transformers.AutoModelForCausalLM.from_pretrained(MODEL_ID)) + tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_ID) + +elif args.model == 'codellama': + import torch + + assert torch.cuda.is_available() + + MODEL_ID = 'codellama/CodeLlama-7b-Instruct-hf' + hfppl_llm = CachedCausalLM.from_pretrained(MODEL_ID, load_in_8bit=False) + tokenizer = AutoTokenizer.from_pretrained( + MODEL_ID, + use_fast=True, + eot_token=None, + fill_token=None, + prefix_token=None, + middle_token=None, + suffix_token=None, + ) + +else: + raise ValueError(args.model) + + +prompt_template = """ +You have access to a political survey data table named "data", which includes the following columns: +- "age" (integer) +- "gender" ("male" or "female"), +- "year" (integer) +- "state_color" ("blue" or "red") +- "zipcode" (integer) +- "vote" ("democrat" or "republican") +- "registered_party" ("democrat" or "republican") +- "race_ethnicity" ("white", "black", or "latino"). + +Q: Write a SQL query that shows individuals' age and gender, for people over 50 years old. +A: SELECT age, gender FROM data WHERE age>50 +Q: Write a SQL query that shows individuals' vote and zipcode, ordered from lowest to highest age. +A: SELECT vote, zipcode, age FROM data ORDER BY age ASC +Q: %s +A:""" + +grammar = r""" + +start: WS? "SELECT" WS select_expr WS "FROM" WS from_expr [WS "WHERE" WS bool_condition] [WS "GROUP BY" WS var_list] [WS "ORDER BY" WS orderby_expr] WS EOS +EOS: "" +select_expr: STAR | select_list +bool_condition: bool_expr | "(" bool_condition WS "AND" WS bool_condition ")" | "(" bool_condition WS "OR" WS bool_condition ")" +bool_expr: var "=" value | var ">" value | var "<" value +from_expr: "data" +orderby_expr: var_list WS "ASC" | var_list WS "DESC" +select_list: select_var ("," WS select_var)* +var_list: var ("," WS var)* +select_var: var | "AVG(" var ")" | "MEDIAN(" var ")" | "COUNT(" var ")" +var: "age" | "gender" | "year" | "state_color" | "zipcode" | "vote" | "race_ethnicity" +value: NUMBER | "'red'" | "'blue'" | "'white'" | "'black'" | "'latino'" | "'republican'" | "'democrat'" | "'male'" | "'female'" +STAR: "*" +NUMBER: /\d+/ +WS: /[ \n\r\t]+/ + +""" + + +prompts = [ + "Write a SQL query that returns white voters' average age for each state color and sort the results.", + 'Write a SQL query that shows the young republicans.', + 'Write a SQL query that shows the old democrats in Williamsburg.', + 'Write a SQL query that shows the oldest democrat in each red state.', + 'Write a SQL query that shows the average age of red states vs blue states.', +] + + +def main(): + character_cfg = LarkStuff(grammar).char_cfg(0.99, ignore='[ ]?') + + guide = EarleyBoolMaskCFGLM(character_cfg) + + BATCH_SIZE = 80 + + hfppl_llm.batch_size = BATCH_SIZE + genparse_llm = AsyncGreedilyTokenizedLLM( + model=hfppl_llm, tokenizer=tokenizer, batch_size=BATCH_SIZE + ) + + guide = EarleyBoolMaskCFGLM(LarkStuff(grammar).char_cfg(0.99, ignore='[ ]?')) + sampler = HFPPLSampler(llm=genparse_llm, guide=guide) + if args.proposal == 'character': + proposal = CharacterProposal(llm=genparse_llm, guide=guide) + elif args.proposal == 'token': + proposal = TokenProposal(llm=genparse_llm, guide=guide, K=5) + else: + raise ValueError(f'invalid proposal name {args.proposal!r}') + + for _ in range(args.reps): + for sql_prompt in prompts: + prompt = prompt_template % sql_prompt + print(colors.cyan % colors.line(100)) + print(colors.cyan % sql_prompt) + + particles, record = sampler.run_inference( + prompt=prompt, + proposal=proposal, + method=args.inference, + n_particles=args.particles, + max_tokens=args.max_tokens, + verbosity=args.verbosity, + return_record=True, + ) + + if args.particles > 1 and record is not None: + fig = record.plot_particles_trajectory() + fig.write_html('viz.html') + print('wrote to viz.html') + + print(colors.yellow % 'character posterior') + posterior = Float.chart() + for p in particles: + posterior[''.join(p.context).strip()] += np.exp(p.weight) + print(posterior.normalize()) + + if 0: + print(colors.yellow % 'token posterior') + posterior = Float.chart() + for p in particles: + posterior[tuple(p.context)] += np.exp(p.weight) + print(posterior.normalize()) + + proposal.timer.plot_feature('t') + with open('runtime.pkl', 'wb') as f: + pickle.dump(proposal.timer, f) + print('wrote to runtime.pkl') + + import pylab as pl + + pl.title(args) + pl.xlabel('context size (characters)') + pl.savefig('runtime.pdf') + print('wrote to runtime.pdf') + pl.show() + + # from arsenal.debug import ip + # ip() + + +if __name__ == '__main__': + main() From e5ff3de013c85ed5e8091994a7470f60c248e2df Mon Sep 17 00:00:00 2001 From: Tianyu Liu Date: Thu, 20 Jun 2024 01:16:44 +0200 Subject: [PATCH 02/35] vllm particle --- benchmark/benchmark_vllm_inference.py | 181 +++++++++++++++++++++----- genparse/steer.py | 147 ++++++++++++++++++++- 2 files changed, 297 insertions(+), 31 deletions(-) diff --git a/benchmark/benchmark_vllm_inference.py b/benchmark/benchmark_vllm_inference.py index 038835b7..d5005a39 100644 --- a/benchmark/benchmark_vllm_inference.py +++ b/benchmark/benchmark_vllm_inference.py @@ -15,7 +15,7 @@ from genparse.steer import HFPPLSampler from genparse.util import LarkStuff -import vllm +# from vllm import LLM, LLMEngine p = ArgumentParser() p.add_argument('--model', choices=['gpt2', 'codellama'], required=True) @@ -40,38 +40,159 @@ # TODO: -# replace hfppl_llm with vllm +# 1. replace hfppl_llm with vllm # need to implement p_next / next_token_logprobs for vllm +# 2. write VLLMParticle, VLLMSampler in steer.py -# class vllmppl_llm(vllm): -# ... -# def next_token_logprobs(xs): -# ... - -class vllmppl_llm(vllm.VLLM): - - def next_token_logprobs(self, xs): +import vllm +from typing import Optional, List, Union +import time +from vllm.engine.output_processor.util import create_output_by_sequence_group +from vllm.engine.arg_utils import EngineArgs +from vllm.utils import Counter +from vllm.usage.usage_lib import UsageContext +from vllm.outputs import (EmbeddingRequestOutput, RequestOutput, + RequestOutputFactory) +from vllm.sequence import ExecuteModelRequest + + +class ppl_LLMEngine(vllm.LLMEngine): + def next_token_logprobs(self): + logits, output = self.model_executor.execute_model( + execute_model_req=self.execute_model_req) + + return logits.log_softmax(dim=-1) + + + # def _process_model_outputs( + # self, + # output, + # scheduled_seq_groups, + # ignored_seq_groups, + # seq_group_metadata_list, + # ): + # """Apply the model output to the sequences in the scheduled seq groups. + # Returns RequestOutputs that can be returned to the client. + # """ + # now = time.time() + + # # Organize outputs by [sequence group][step] instead of + # # [step][sequence group]. + # output_by_sequence_group = create_output_by_sequence_group( + # output, num_seq_groups=len(scheduled_seq_groups)) + + # # Update the scheduled sequence groups with the model outputs. + # for scheduled_seq_group, outputs, seq_group_meta in zip( + # scheduled_seq_groups, output_by_sequence_group, + # seq_group_metadata_list): + # seq_group = scheduled_seq_group.seq_group + # seq_group.update_num_computed_tokens( + # scheduled_seq_group.token_chunk_size) + + # # self.output_processor.process_prompt_logprob(seq_group, outputs) + # # if seq_group_meta.do_sample: + # # # This is important. Update the generator state with sampled + # # # token. Should leave the selection of next token to hfppl + # # self.output_processor.process_outputs(seq_group, outputs) + + # # Free the finished sequence groups. + # self.scheduler.free_finished_seq_groups() + + # # # Create the outputs. + # # request_outputs = [] + # # for scheduled_seq_group in scheduled_seq_groups: + # # seq_group = scheduled_seq_group.seq_group + # # seq_group.maybe_set_first_token_time(now) + # # request_output = RequestOutputFactory.create(seq_group) + # # request_outputs.append(request_output) + # # for seq_group in ignored_seq_groups: + # # request_output = RequestOutputFactory.create(seq_group) + # # request_outputs.append(request_output) + # return request_outputs + +class LogitsSampler(torch.nn.Module): + def __init__(self, base_sampler): + self.base_sampler = base_sampler + super().__init__() + + self.include_gpu_probs_tensor = False + + def forward( + self, + logits, + sampling_metadata, + ): + return logits, self.base_sampler(logits, sampling_metadata) + + +class vllmppl_llm(vllm.LLM): + + def __init__( + self, + model: str, + tokenizer: Optional[str] = None, + tokenizer_mode: str = "auto", + skip_tokenizer_init: bool = False, + trust_remote_code: bool = False, + tensor_parallel_size: int = 1, + dtype: str = "auto", + quantization: Optional[str] = None, + revision: Optional[str] = None, + tokenizer_revision: Optional[str] = None, + seed: int = 0, + gpu_memory_utilization: float = 0.9, + swap_space: int = 4, + enforce_eager: bool = False, + max_context_len_to_capture: Optional[int] = None, + max_seq_len_to_capture: int = 8192, + disable_custom_all_reduce: bool = False, + **kwargs, + ) -> None: + if "disable_log_stats" not in kwargs: + kwargs["disable_log_stats"] = True + engine_args = EngineArgs( + model=model, + tokenizer=tokenizer, + tokenizer_mode=tokenizer_mode, + skip_tokenizer_init=skip_tokenizer_init, + trust_remote_code=trust_remote_code, + tensor_parallel_size=tensor_parallel_size, + dtype=dtype, + quantization=quantization, + revision=revision, + tokenizer_revision=tokenizer_revision, + seed=seed, + gpu_memory_utilization=gpu_memory_utilization, + swap_space=swap_space, + enforce_eager=enforce_eager, + max_context_len_to_capture=max_context_len_to_capture, + max_seq_len_to_capture=max_seq_len_to_capture, + disable_custom_all_reduce=disable_custom_all_reduce, + **kwargs, + ) + self.llm_engine = ppl_LLMEngine.from_engine_args( + engine_args, usage_context=UsageContext.LLM_CLASS) + # the sampler of the model + self.llm_engine.model_executor.driver_worker.model_runner.model.sampler = LogitsSampler( + self.llm_engine.model_executor.driver_worker.model_runner.model.sampler + ) + self.request_counter = Counter() + self.eos = self.llm_engine.model_executor.driver_worker.model_runner.model.eos_token_id + + # TODO: + # modify methods to get the next token logprobs + # the core model + self.llm_engine.model_executor.driver_worker.model_runner.model + self.llm_engine._process_model_outputs + + def next_token_logprobs(self,): + return self.llm_engine.next_token_logprobs() + + def p_next(self, input_ids): # call the vllm engine of VLLM - step_outputs = self.llm_engine.step() - # TODO: - # Check what's in step_outputs - for output in step_outputs: - if output.finished: - outputs.append(output) - if use_tqdm: - if isinstance(output, RequestOutput): - # Calculate tokens only for RequestOutput - total_in_toks += len(output.prompt_token_ids) - in_spd = total_in_toks / pbar.format_dict["elapsed"] - total_out_toks += sum( - len(stp.token_ids) for stp in output.outputs) - out_spd = total_out_toks / pbar.format_dict[ - "elapsed"] - pbar.postfix = ( - f"est. speed input: {in_spd:.2f} toks/s, " - f"output: {out_spd:.2f} toks/s") - pbar.update(1) - return self.model(xs) + return self.next_token_logprobs().exp() + + if args.model == 'gpt2': import transformers diff --git a/genparse/steer.py b/genparse/steer.py index cfdd3fd6..8d13b8f1 100644 --- a/genparse/steer.py +++ b/genparse/steer.py @@ -201,12 +201,157 @@ def __repr__(self): raise AssertionError(METHOD) +class VLLMParticle(Model): + def __init__(self, llm, guide, proposal, prompt, max_tokens, verbosity=0): + from vllm.sampling_params import SamplingParams + + super().__init__() + self.llm = llm # type: VLLM + # call the vllm engine of VLLM + inputs = self.llm._convert_v1_inputs(prompts=prompt, prompt_token_ids=None) + # add request for prompt + self.llm._validate_and_add_requests( + inputs=inputs, + params=SamplingParams(), # using default params because we only rely on logits + lora_request=None + ) + + self.guide = guide + self.prompt = prompt + self.context = [] + self.proposal = proposal + self.max_tokens = max_tokens + self.verbosity = verbosity + + async def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: + from vllm.sequence import ExecuteModelRequest + seq_group_metadata_list, scheduler_outputs = self.llm.llm_engine.scheduler.schedule() + + self.llm.llm_engine.execute_model_req = ExecuteModelRequest( + seq_group_metadata_list=seq_group_metadata_list, + blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in, + blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out, + blocks_to_copy=scheduler_outputs.blocks_to_copy, + num_lookahead_slots=scheduler_outputs.num_lookahead_slots, + running_queue_size=scheduler_outputs.running_queue_size, + ) + + # executing: + (token, weight_update) = await self.proposal.sample_next_token( + prompt=self.prompt, + context=''.join(self.context), + compare_time=(self.verbosity > 1), + ) + self.context.append(token) + self.weight += np.log(weight_update) + self.max_tokens -= 1 + if self.verbosity > 1: + print(f"`{token}` : {''.join(self.context)} : {self.weight}") + + # TODO: fit token back into SampleOutput, so vllm can + # process it correctly + output = SamplerOutput( + outputs=sampler_output, + ) + + # post-processing. Do after sample is chosen + request_outputs = self.llm.llm_engine._process_model_outputs( + output, scheduler_outputs.scheduled_seq_groups, + scheduler_outputs.ignored_seq_groups, seq_group_metadata_list) + + # Log stats. + self.llm.llm_engine.do_log_stats(scheduler_outputs, output) + + if not request_outputs: + self.llm.llm_engine.model_executor.stop_remote_worker_execution_loop() + + # if token == self.llm.eos or self.max_tokens == 0 or token == EOS: + # self.finish() + # return + + return request_outputs + + def immutable_properties(self): + return ['llm', 'prompt', 'guide', 'verbosity'] + + def __repr__(self): + return f"`{'' if not self.context else self.context[-1]}` : {''.join(self.context)} : {self.weight}" + + def __str__(self): + return ''.join(self.context) + + +class VLLMSampler: + def __init__(self, llm, guide): + """ + Args: + llm (vllm.VLLM) + guide (LM) + Returns: + particle_approximation (ParticleApproximation) + record (dict | NoneType): information about the run + """ + self.llm = llm + self.guide = guide + + def run_inference( + self, + prompt, + proposal, + method, + n_particles, + n_beam=None, + max_tokens=float('inf'), + verbosity=0, + return_record=False, + seed=None, + ): + if seed is not None: + set_seed(seed) + + model = VLLMParticle( + llm=self.llm, + guide=self.guide, + prompt=prompt, + proposal=proposal, + max_tokens=max_tokens, + verbosity=verbosity, + ) + + record = None + if method == 'smc-steer': + assert n_beam is not None + if return_record: + raise Warning('Record not yet implemented for smc-steer') + particles = asyncio.run( + smc_steer(model, n_particles=n_particles, n_beam=n_beam) + ) + + elif method == 'smc-standard': + if return_record: + particles, record = asyncio.run( + smc_standard_record( + model, n_particles=n_particles, return_record=return_record + ) + ) + else: + particles = asyncio.run(smc_standard(model, n_particles=n_particles)) + + elif method == 'importance-sampling': + particles = asyncio.run(importance_sampling(model, n_particles=n_particles)) + + else: + raise ValueError(f'Unknown inference method: {method}.') + + return ParticleApproximation(particles), record + + + # ____________________________________________________________________________________ # Approximate inference with HFPPL # This code is still experimental and actively being developed # TODO: write tests - class HFPPLParticle(Model): """ Simple HFPPL model (particle). From 4e059a04c7ffcc180ab3cc2b8c4af97a80b41d26 Mon Sep 17 00:00:00 2001 From: Tianyu Liu Date: Thu, 20 Jun 2024 12:59:50 +0000 Subject: [PATCH 03/35] vllm base --- benchmark/benchmark_vllm_inference.py | 186 ++------------------------ genparse/lm.py | 8 +- genparse/proposal/character.py | 3 +- genparse/steer.py | 70 +++++++--- genparse/vllm_compatibility.py | 156 +++++++++++++++++++++ 5 files changed, 227 insertions(+), 196 deletions(-) create mode 100644 genparse/vllm_compatibility.py diff --git a/benchmark/benchmark_vllm_inference.py b/benchmark/benchmark_vllm_inference.py index d5005a39..ab52687e 100644 --- a/benchmark/benchmark_vllm_inference.py +++ b/benchmark/benchmark_vllm_inference.py @@ -11,10 +11,12 @@ from genparse import Float from genparse.cfglm import EarleyBoolMaskCFGLM from genparse.lm import AsyncGreedilyTokenizedLLM +from genparse.vllm_compatibility import vllmpplLLM from genparse.proposal import CharacterProposal, TokenProposal -from genparse.steer import HFPPLSampler +from genparse.steer import HFPPLSampler, VLLMSampler from genparse.util import LarkStuff +import torch # from vllm import LLM, LLMEngine p = ArgumentParser() @@ -37,162 +39,7 @@ set_seed(RANDOM_SEED) seed(RANDOM_SEED) manual_seed(RANDOM_SEED) - - -# TODO: -# 1. replace hfppl_llm with vllm -# need to implement p_next / next_token_logprobs for vllm -# 2. write VLLMParticle, VLLMSampler in steer.py - -import vllm -from typing import Optional, List, Union -import time -from vllm.engine.output_processor.util import create_output_by_sequence_group -from vllm.engine.arg_utils import EngineArgs -from vllm.utils import Counter -from vllm.usage.usage_lib import UsageContext -from vllm.outputs import (EmbeddingRequestOutput, RequestOutput, - RequestOutputFactory) -from vllm.sequence import ExecuteModelRequest - - -class ppl_LLMEngine(vllm.LLMEngine): - def next_token_logprobs(self): - logits, output = self.model_executor.execute_model( - execute_model_req=self.execute_model_req) - - return logits.log_softmax(dim=-1) - - - # def _process_model_outputs( - # self, - # output, - # scheduled_seq_groups, - # ignored_seq_groups, - # seq_group_metadata_list, - # ): - # """Apply the model output to the sequences in the scheduled seq groups. - # Returns RequestOutputs that can be returned to the client. - # """ - # now = time.time() - - # # Organize outputs by [sequence group][step] instead of - # # [step][sequence group]. - # output_by_sequence_group = create_output_by_sequence_group( - # output, num_seq_groups=len(scheduled_seq_groups)) - - # # Update the scheduled sequence groups with the model outputs. - # for scheduled_seq_group, outputs, seq_group_meta in zip( - # scheduled_seq_groups, output_by_sequence_group, - # seq_group_metadata_list): - # seq_group = scheduled_seq_group.seq_group - # seq_group.update_num_computed_tokens( - # scheduled_seq_group.token_chunk_size) - - # # self.output_processor.process_prompt_logprob(seq_group, outputs) - # # if seq_group_meta.do_sample: - # # # This is important. Update the generator state with sampled - # # # token. Should leave the selection of next token to hfppl - # # self.output_processor.process_outputs(seq_group, outputs) - - # # Free the finished sequence groups. - # self.scheduler.free_finished_seq_groups() - - # # # Create the outputs. - # # request_outputs = [] - # # for scheduled_seq_group in scheduled_seq_groups: - # # seq_group = scheduled_seq_group.seq_group - # # seq_group.maybe_set_first_token_time(now) - # # request_output = RequestOutputFactory.create(seq_group) - # # request_outputs.append(request_output) - # # for seq_group in ignored_seq_groups: - # # request_output = RequestOutputFactory.create(seq_group) - # # request_outputs.append(request_output) - # return request_outputs - -class LogitsSampler(torch.nn.Module): - def __init__(self, base_sampler): - self.base_sampler = base_sampler - super().__init__() - - self.include_gpu_probs_tensor = False - - def forward( - self, - logits, - sampling_metadata, - ): - return logits, self.base_sampler(logits, sampling_metadata) - - -class vllmppl_llm(vllm.LLM): - - def __init__( - self, - model: str, - tokenizer: Optional[str] = None, - tokenizer_mode: str = "auto", - skip_tokenizer_init: bool = False, - trust_remote_code: bool = False, - tensor_parallel_size: int = 1, - dtype: str = "auto", - quantization: Optional[str] = None, - revision: Optional[str] = None, - tokenizer_revision: Optional[str] = None, - seed: int = 0, - gpu_memory_utilization: float = 0.9, - swap_space: int = 4, - enforce_eager: bool = False, - max_context_len_to_capture: Optional[int] = None, - max_seq_len_to_capture: int = 8192, - disable_custom_all_reduce: bool = False, - **kwargs, - ) -> None: - if "disable_log_stats" not in kwargs: - kwargs["disable_log_stats"] = True - engine_args = EngineArgs( - model=model, - tokenizer=tokenizer, - tokenizer_mode=tokenizer_mode, - skip_tokenizer_init=skip_tokenizer_init, - trust_remote_code=trust_remote_code, - tensor_parallel_size=tensor_parallel_size, - dtype=dtype, - quantization=quantization, - revision=revision, - tokenizer_revision=tokenizer_revision, - seed=seed, - gpu_memory_utilization=gpu_memory_utilization, - swap_space=swap_space, - enforce_eager=enforce_eager, - max_context_len_to_capture=max_context_len_to_capture, - max_seq_len_to_capture=max_seq_len_to_capture, - disable_custom_all_reduce=disable_custom_all_reduce, - **kwargs, - ) - self.llm_engine = ppl_LLMEngine.from_engine_args( - engine_args, usage_context=UsageContext.LLM_CLASS) - # the sampler of the model - self.llm_engine.model_executor.driver_worker.model_runner.model.sampler = LogitsSampler( - self.llm_engine.model_executor.driver_worker.model_runner.model.sampler - ) - self.request_counter = Counter() - self.eos = self.llm_engine.model_executor.driver_worker.model_runner.model.eos_token_id - - # TODO: - # modify methods to get the next token logprobs - # the core model - self.llm_engine.model_executor.driver_worker.model_runner.model - self.llm_engine._process_model_outputs - - def next_token_logprobs(self,): - return self.llm_engine.next_token_logprobs() - - def p_next(self, input_ids): - # call the vllm engine of VLLM - return self.next_token_logprobs().exp() - - + if args.model == 'gpt2': import transformers @@ -200,25 +47,18 @@ def p_next(self, input_ids): from genparse.lm import LLM MODEL_ID = 'gpt2' - hfppl_llm = LLM(transformers.AutoModelForCausalLM.from_pretrained(MODEL_ID)) + hfppl_llm = vllmpplLLM(MODEL_ID) tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_ID) elif args.model == 'codellama': + import transformers import torch assert torch.cuda.is_available() MODEL_ID = 'codellama/CodeLlama-7b-Instruct-hf' - hfppl_llm = CachedCausalLM.from_pretrained(MODEL_ID, load_in_8bit=False) - tokenizer = AutoTokenizer.from_pretrained( - MODEL_ID, - use_fast=True, - eot_token=None, - fill_token=None, - prefix_token=None, - middle_token=None, - suffix_token=None, - ) + hfppl_llm = vllmpplLLM(MODEL_ID, dtype=torch.float32, max_model_len=4096) + tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_ID) else: raise ValueError(args.model) @@ -285,7 +125,7 @@ def main(): ) guide = EarleyBoolMaskCFGLM(LarkStuff(grammar).char_cfg(0.99, ignore='[ ]?')) - sampler = HFPPLSampler(llm=genparse_llm, guide=guide) + sampler = VLLMSampler(llm=genparse_llm, guide=guide) if args.proposal == 'character': proposal = CharacterProposal(llm=genparse_llm, guide=guide) elif args.proposal == 'token': @@ -328,16 +168,16 @@ def main(): print(posterior.normalize()) proposal.timer.plot_feature('t') - with open('runtime.pkl', 'wb') as f: + with open('vllm_runtime.pkl', 'wb') as f: pickle.dump(proposal.timer, f) - print('wrote to runtime.pkl') + print('wrote to vllm_runtime.pkl') import pylab as pl pl.title(args) pl.xlabel('context size (characters)') - pl.savefig('runtime.pdf') - print('wrote to runtime.pdf') + pl.savefig('vllm_runtime.pdf') + print('wrote to vllm_runtime.pdf') pl.show() # from arsenal.debug import ip diff --git a/genparse/lm.py b/genparse/lm.py index e6a616ef..06e5bb65 100644 --- a/genparse/lm.py +++ b/genparse/lm.py @@ -10,6 +10,7 @@ from genparse.semiring import Float from genparse.tokenization import decode_tokenizer_vocab +from genparse.vllm_compatibility import vllmpplLLM class LM: @@ -263,11 +264,14 @@ def __call__(self, xs): async def next_token_logprobs(self, xs, top=None): return self.p_next(xs, top=top).map_values(np.log) - async def p_next(self, xs, top=None): + async def p_next(self, xs, top=None, **kwargs): assert isinstance(xs, str) tokens = self.tokenizer.encode(xs) - _logp = await self._model.next_token_logprobs(tokens) + if isinstance(self._model, vllmpplLLM): + _logp = await self._model.next_token_logprobs(tokens, **kwargs) + else: + _logp = await self._model.next_token_logprobs(tokens) _logp = _logp.cpu().numpy() if hasattr(_logp, 'cpu') else _logp _p = np.exp(_logp) diff --git a/genparse/proposal/character.py b/genparse/proposal/character.py index 541530cf..b819c10d 100644 --- a/genparse/proposal/character.py +++ b/genparse/proposal/character.py @@ -88,6 +88,7 @@ async def sample_next_token( verbosity=0, compare_time=False, correct_weights=True, + execute_model_req=None, **kwargs, ): """ @@ -105,7 +106,7 @@ async def sample_next_token( weight_update : Incremental SMC weight update. """ with self.timer['llm'](t=len(context)): - p_llm = await self.llm.p_next(prompt + context) + p_llm = await self.llm.p_next(prompt + context, execute_model_req=execute_model_req) with self.timer['cfg+trie'](t=len(context)): self._update_trie(p_llm) if correct_weights: diff --git a/genparse/steer.py b/genparse/steer.py index 8d13b8f1..5ae6490a 100644 --- a/genparse/steer.py +++ b/genparse/steer.py @@ -204,30 +204,55 @@ def __repr__(self): class VLLMParticle(Model): def __init__(self, llm, guide, proposal, prompt, max_tokens, verbosity=0): from vllm.sampling_params import SamplingParams + from genparse.tokenization import decode_tokenizer_vocab + super().__init__() - self.llm = llm # type: VLLM + self.llm = llm # type: async + # self.llm._model: VLLM # call the vllm engine of VLLM - inputs = self.llm._convert_v1_inputs(prompts=prompt, prompt_token_ids=None) + inputs = self.llm._model._convert_v1_inputs(prompts=prompt, prompt_token_ids=None) # add request for prompt - self.llm._validate_and_add_requests( + self.llm._model._validate_and_add_requests( inputs=inputs, - params=SamplingParams(), # using default params because we only rely on logits + # using default params because we only rely on logits + params=SamplingParams(max_tokens=max_tokens, stop_token_ids=[self.llm.eos]), lora_request=None ) + self.token_to_id = {x: i for i, x in enumerate(decode_tokenizer_vocab(self.llm.tokenizer))} self.guide = guide self.prompt = prompt self.context = [] + + self.sampler_output = [] + self.proposal = proposal self.max_tokens = max_tokens + + self.verbosity = verbosity - async def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: - from vllm.sequence import ExecuteModelRequest - seq_group_metadata_list, scheduler_outputs = self.llm.llm_engine.scheduler.schedule() - self.llm.llm_engine.execute_model_req = ExecuteModelRequest( + async def step(self): + from vllm.sequence import ( + CompletionSequenceGroupOutput, SequenceOutput, + ExecuteModelRequest, SamplerOutput, Logprob + ) + + seq_group_metadata_list, scheduler_outputs = self.llm._model.llm_engine.scheduler.schedule() + + if scheduler_outputs.is_empty(): + # finishing due to resource + self.llm._model.llm_engine._process_model_outputs( + [], scheduler_outputs.scheduled_seq_groups, + scheduler_outputs.ignored_seq_groups, seq_group_metadata_list) + self.llm._model.llm_engine.model_executor.stop_remote_worker_execution_loop() + self.finish() + + return + + execute_model_req = ExecuteModelRequest( seq_group_metadata_list=seq_group_metadata_list, blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in, blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out, @@ -241,35 +266,40 @@ async def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]: prompt=self.prompt, context=''.join(self.context), compare_time=(self.verbosity > 1), + execute_model_req=execute_model_req ) + token_id = self.token_to_id.get(token, self.llm._model.eos_token_id) self.context.append(token) self.weight += np.log(weight_update) self.max_tokens -= 1 + print("token, token_id", token, token_id) if self.verbosity > 1: + print(f"`{token}` : {''.join(self.context)} : {self.weight}") # TODO: fit token back into SampleOutput, so vllm can # process it correctly - output = SamplerOutput( - outputs=sampler_output, - ) + output = [] + for seq_group_md in seq_group_metadata_list: + parent_seq_id = list(seq_group_md.seq_data.keys())[0] + output.append(SamplerOutput( + outputs=[ + CompletionSequenceGroupOutput( + samples=[SequenceOutput(parent_seq_id=parent_seq_id, output_token=token_id, logprobs={token_id: Logprob(logprob=np.log(weight_update))})], + prompt_logprobs=None)] + )) + self.sampler_output = self.sampler_output + output # post-processing. Do after sample is chosen - request_outputs = self.llm.llm_engine._process_model_outputs( + self.llm._model.llm_engine._process_model_outputs( output, scheduler_outputs.scheduled_seq_groups, scheduler_outputs.ignored_seq_groups, seq_group_metadata_list) # Log stats. - self.llm.llm_engine.do_log_stats(scheduler_outputs, output) - - if not request_outputs: - self.llm.llm_engine.model_executor.stop_remote_worker_execution_loop() + self.llm._model.llm_engine.do_log_stats(scheduler_outputs, output) - # if token == self.llm.eos or self.max_tokens == 0 or token == EOS: - # self.finish() - # return + return - return request_outputs def immutable_properties(self): return ['llm', 'prompt', 'guide', 'verbosity'] diff --git a/genparse/vllm_compatibility.py b/genparse/vllm_compatibility.py new file mode 100644 index 00000000..8de0267d --- /dev/null +++ b/genparse/vllm_compatibility.py @@ -0,0 +1,156 @@ + +# TODO: +# 1. replace hfppl_llm with vllm [Done] +# need to implement p_next / next_token_logprobs for vllm +# 2. write VLLMParticle, VLLMSampler in steer.py +import torch + +import vllm +from typing import Optional, List, Union +import time +from vllm.engine.output_processor.util import create_output_by_sequence_group +from vllm.engine.arg_utils import EngineArgs +from vllm.utils import Counter +from vllm.usage.usage_lib import UsageContext +from vllm.outputs import (EmbeddingRequestOutput, RequestOutput, + RequestOutputFactory) +from vllm.sequence import ExecuteModelRequest + + +class LogitsSampler(torch.nn.Module): + def __init__(self, base_sampler): + super().__init__() + + self.base_sampler = base_sampler + self.include_gpu_probs_tensor = False + + def forward( + self, + logits, + sampling_metadata, + ): + + return logits + + +class pplLMEngine(vllm.LLMEngine): + async def next_token_logprobs(self, execute_model_req): + logits = self.model_executor.execute_model( + execute_model_req=execute_model_req) + + # tensor([[-73.3893, -75.2677, -74.1485, ..., -81.7179, -80.7128, -69.3829]], + # device='cuda:0') + # SamplerOutput(outputs=[CompletionSequenceGroupOutput(samples=[SequenceOutput(parent_seq_id=0, output_token=33493, logprobs={33493: Logprob(logprob=-0.14365723729133606, rank=1, decoded_token=None)})], prompt_logprobs=None)], sampled_token_probs=None, sampled_token_ids=None, spec_decode_worker_metrics=None) + + return logits[0][0].log_softmax(dim=-1).float() #.tolist() + + def _process_model_outputs( + self, + output, + scheduled_seq_groups, + ignored_seq_groups, + seq_group_metadata_list, + ): + now = time.time() + """Apply the model output to the sequences in the scheduled seq groups. + Returns RequestOutputs that can be returned to the client. + """ + # Organize outputs by [sequence group][step] instead of + # [step][sequence group]. + output_by_sequence_group = create_output_by_sequence_group( + output, num_seq_groups=len(scheduled_seq_groups)) + # Update the scheduled sequence groups with the model outputs. + for scheduled_seq_group, outputs, seq_group_meta in zip( + scheduled_seq_groups, output_by_sequence_group, + seq_group_metadata_list): + seq_group = scheduled_seq_group.seq_group + seq_group.update_num_computed_tokens( + scheduled_seq_group.token_chunk_size) + + self.output_processor.process_prompt_logprob(seq_group, outputs) + if seq_group_meta.do_sample: + # This is important. Update the generator state with sampled + # token. Should leave the selection of next token to hfppl + self.output_processor.process_outputs(seq_group, outputs) + + # Free the finished sequence groups. + self.scheduler.free_finished_seq_groups() + + # # Create the outputs. + request_outputs = [] + for scheduled_seq_group in scheduled_seq_groups: + seq_group = scheduled_seq_group.seq_group + seq_group.maybe_set_first_token_time(now) + request_output = RequestOutputFactory.create(seq_group) + request_outputs.append(request_output) + for seq_group in ignored_seq_groups: + request_output = RequestOutputFactory.create(seq_group) + request_outputs.append(request_output) + return # request_outputs + + + +class vllmpplLLM(vllm.LLM): + + def __init__( + self, + model: str, + tokenizer: Optional[str] = None, + tokenizer_mode: str = "auto", + skip_tokenizer_init: bool = False, + trust_remote_code: bool = False, + tensor_parallel_size: int = 1, + dtype: str = "auto", + quantization: Optional[str] = None, + revision: Optional[str] = None, + tokenizer_revision: Optional[str] = None, + seed: int = 0, + gpu_memory_utilization: float = 0.9, + swap_space: int = 4, + enforce_eager: bool = False, + max_context_len_to_capture: Optional[int] = None, + max_seq_len_to_capture: int = 8192, + disable_custom_all_reduce: bool = False, + **kwargs, + ) -> None: + + if "disable_log_stats" not in kwargs: + kwargs["disable_log_stats"] = True + engine_args = EngineArgs( + model=model, + tokenizer=tokenizer, + tokenizer_mode=tokenizer_mode, + skip_tokenizer_init=skip_tokenizer_init, + trust_remote_code=trust_remote_code, + tensor_parallel_size=tensor_parallel_size, + dtype=dtype, + quantization=quantization, + revision=revision, + tokenizer_revision=tokenizer_revision, + seed=seed, + gpu_memory_utilization=gpu_memory_utilization, + swap_space=swap_space, + enforce_eager=enforce_eager, + max_context_len_to_capture=max_context_len_to_capture, + max_seq_len_to_capture=max_seq_len_to_capture, + disable_custom_all_reduce=disable_custom_all_reduce, + **kwargs, + ) + self.llm_engine = pplLMEngine.from_engine_args( + engine_args, usage_context=UsageContext.LLM_CLASS) + # sampler of the model + self.llm_engine.model_executor.driver_worker.model_runner.model.sampler = LogitsSampler( + self.llm_engine.model_executor.driver_worker.model_runner.model.sampler + ) + self.request_counter = Counter() + # self.eos = self.llm_engine.model_executor.driver_worker.model_runner.model.eos_token_id + self.eos_token_id = self.llm_engine._get_eos_token_id(lora_request=None) + print("self.eos_token_id", self.eos_token_id) + + + def next_token_logprobs(self, input_ids, **kwargs): + # call the vllm engine of VLLM + return self.llm_engine.next_token_logprobs(**kwargs) + + def p_next(self, input_ids, **kwargs): + return self.next_token_logprobs(**kwargs).exp() From d98994b23406fc0c02367b48629483e4f4ea084c Mon Sep 17 00:00:00 2001 From: benlipkin Date: Wed, 19 Jun 2024 09:31:47 -0400 Subject: [PATCH 04/35] more makefile cleanup --- Makefile | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index f187c371..b6dc5ffe 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,12 @@ SHELL := /usr/bin/env bash EXEC = python=3.10 NAME = genparse +TEST = tests RUN = python -m INSTALL = $(RUN) pip install +SRC_FILES := $(shell find $(NAME) -name '*.py') +TEST_FILES := $(shell find $(TEST) -name '*.py') .DEFAULT_GOAL := help -SRC_FILES := $(shell find genparse -name '*.py') -TEST_FILES := $(shell find tests -name '*.py') ## help : print available commands. .PHONY : help @@ -31,7 +32,7 @@ format : env ## docs : build documentation. .PHONY : docs docs : env html/docs/index.html -html/docs/index.html : $(NAME)/*.py +html/docs/index.html : $(SRC_FILES) @pdoc $(NAME) -o $(@D) ## test : run linting and tests. @@ -43,4 +44,4 @@ pytest : env html/coverage/index.html html/coverage/index.html : html/pytest/report.html @coverage html -d $(@D) html/pytest/report.html : $(SRC_FILES) $(TEST_FILES) - @coverage run --branch -m pytest --html=$@ --self-contained-html tests/ genparse/ + @coverage run --branch -m pytest --html=$@ --self-contained-html $(TEST)/ $(NAME)/ From 0eafb436c7e95dc4ec5a3faf733a895149b383f4 Mon Sep 17 00:00:00 2001 From: Tim Vieira Date: Wed, 19 Jun 2024 13:24:39 -0400 Subject: [PATCH 05/35] Fix: Use the `draw` argument in proposal distributions and LM sampling methods. --- genparse/lm.py | 2 +- genparse/proposal/character.py | 22 +- genparse/proposal/token.py | 4 +- genparse/steer.py | 2 +- notes/Token-Alignment.ipynb | 2054 +++++++++++++++++++++++++++++++- 5 files changed, 2024 insertions(+), 60 deletions(-) diff --git a/genparse/lm.py b/genparse/lm.py index 06e5bb65..08886b3d 100644 --- a/genparse/lm.py +++ b/genparse/lm.py @@ -37,7 +37,7 @@ def p_next(self, context): "Compute the (conditional) distribution over the next token given the `prefix`." raise NotImplementedError() - def sample(self, ys=(), draw=sample_dict, prob=False, verbose=0, max_tokens=np.inf): + def sample(self, ys=(), draw=sample_dict, prob=True, verbose=0, max_tokens=np.inf): P = 1.0 t = 0 while True: diff --git a/genparse/proposal/character.py b/genparse/proposal/character.py index b819c10d..890b9cbc 100644 --- a/genparse/proposal/character.py +++ b/genparse/proposal/character.py @@ -53,7 +53,9 @@ def __init__(self, *, llm, guide): super().__init__(words, encode=llm._encode, old_eos=llm.eos, new_eos=guide.eos) - def sample(self, prompt, max_tokens=float('inf'), verbosity=0, **kwargs): + def sample( + self, prompt, max_tokens=float('inf'), verbosity=0, draw=sample_dict, **kwargs + ): context = '' W = 1 t = 0 @@ -65,7 +67,7 @@ def sample(self, prompt, max_tokens=float('inf'), verbosity=0, **kwargs): with self.timer['cfg+trie'](t=len(context)): self._update_trie(p_llm) token, weight_update = self._guided_sample_trie( - self.root, context, verbosity=verbosity, **kwargs + context, verbosity=verbosity, draw=draw, **kwargs ) else: token = self.guide.eos @@ -78,7 +80,6 @@ def sample(self, prompt, max_tokens=float('inf'), verbosity=0, **kwargs): context += token if verbosity > 0: print() - self.timer.compare() return (context, W) async def sample_next_token( @@ -89,6 +90,7 @@ async def sample_next_token( compare_time=False, correct_weights=True, execute_model_req=None, + draw=sample_dict, **kwargs, ): """ @@ -111,11 +113,11 @@ async def sample_next_token( self._update_trie(p_llm) if correct_weights: (token, weight_update) = self._guided_sample_trie( - self.root, context, verbosity=verbosity, **kwargs + context, verbosity=verbosity, draw=draw, **kwargs ) else: (token, weight_update) = self._guided_sample_trie_uncorrected( - self.root, context, verbosity=verbosity, **kwargs + context, verbosity=verbosity, draw=draw, **kwargs ) if compare_time: self.timer.compare() @@ -142,7 +144,7 @@ def __deepcopy__(self, memo): return cpy - def _guided_sample_trie(self, root, context, draw=sample_dict, verbosity=0): + def _guided_sample_trie(self, context, draw, verbosity=0): """ This function samples a token from the trie and computes the incremental weight update. @@ -158,7 +160,7 @@ def _guided_sample_trie(self, root, context, draw=sample_dict, verbosity=0): 5. Set the incremental SMC weight update $w^\prime(x) = \sum_{x \in S} w(x)$ """ - curr = root + curr = self.root path = [] inclusion_prob = 1 # path prefix probability @@ -237,9 +239,7 @@ def _guided_sample_trie(self, root, context, draw=sample_dict, verbosity=0): return (token, weight_update) - def _guided_sample_trie_uncorrected( - self, root, context, draw=sample_dict, verbosity=0 - ): + def _guided_sample_trie_uncorrected(self, context, draw, verbosity=0): """ This function samples a token from the trie and computes the incremental weight update. @@ -248,7 +248,7 @@ def _guided_sample_trie_uncorrected( and S is the path through the trie from which x is sampled. """ - curr = root + curr = self.root path = [] guide_prob = 1 proposal_prob = 1 diff --git a/genparse/proposal/token.py b/genparse/proposal/token.py index 296f5de5..d5b50f82 100644 --- a/genparse/proposal/token.py +++ b/genparse/proposal/token.py @@ -47,7 +47,7 @@ def _p_next(self, context, K=None): return Float.chart(take(K, self.traverse_trie(context, p_llm))).normalize() async def sample_next_token( - self, prompt, context, verbosity=0, compare_time=False, **kwargs + self, prompt, context, verbosity=0, compare_time=False, draw=sample_dict, **kwargs ): with self.timer['llm'](t=len(context)): p_llm = await self.llm.p_next(prompt + context) @@ -56,7 +56,7 @@ async def sample_next_token( Q = Float.chart( take(self.K - 1, self.traverse_trie(context, p_llm)) ).normalize() - token = sample_dict(Q) + token = draw(Q) llm_prob = p_llm[self.old_eos if token == self.new_eos else token] guide_prob = self._p_guide[token] diff --git a/genparse/steer.py b/genparse/steer.py index 5ae6490a..b2500727 100644 --- a/genparse/steer.py +++ b/genparse/steer.py @@ -68,7 +68,7 @@ def __init__(self, lm, **opts): D = Float.chart() while tracer.root.mass > 0: with tracer: - s, p = lm.sample(draw=tracer, prob=True, **opts) + s, p = lm.sample(draw=tracer, **opts) D[s] += p D = Float.chart((k, D[k]) for k in sorted(D)) self.D = D diff --git a/notes/Token-Alignment.ipynb b/notes/Token-Alignment.ipynb index 6f9dc21c..89dc7884 100644 --- a/notes/Token-Alignment.ipynb +++ b/notes/Token-Alignment.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "3d9fb828-28ae-4f5f-8fb7-214f325dfa49", "metadata": {}, "outputs": [], @@ -21,17 +21,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "988df6c8-bbb1-4a56-96c0-50ed95639b28", "metadata": {}, "outputs": [], "source": [ "from genparse import CFGLM\n", "from genparse.cfglm import locally_normalize, EOS\n", - "from genparse.align import CharAlignedCFGLM\n", + "from genparse.proposal import CharacterProposal, TokenProposal\n", "from genparse.util import display_table\n", "from genparse.steer import generation_tree\n", - "from genparse.segmentation import bpe_wfst, segmentation_pfst" + "from genparse.segmentation import bpe_wfst, segmentation_pfst\n", + "from genparse.lm import MockLLM" ] }, { @@ -56,18 +57,18 @@ "\n", "Let $p'$ be a distribution over BPE strings $\\bb \\in \\BB^*$.\n", "\n", - "Let $\\phi\\colon \\BB^* \\to \\AA^*$ be a decoding function. The decoding function satisfies: $\\phi(\\bb \\, \\bb') = \\phi(\\bb) \\, \\phi(\\bb')$ for all $\\bb, \\bb' \\in \\BB^*$." + "Let $\\phi\\colon \\BB^* \\to \\AA^*$ be a decoding function. The decoding function satisfies: $\\phi(\\bb \\, \\bb') = \\phi(\\bb) \\, \\phi(\\bb')$ for all $\\bb, \\bb' \\in \\BB^*$. In other words, $\\phi$ is a **monoid homomorphism**." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "78971f03-773c-43dd-90fe-900cb64b269e", "metadata": {}, "outputs": [], "source": [ "p = CFGLM.from_string(\n", - " \"\"\"\n", + "\"\"\"\n", "\n", "1: S -> a\n", "1: S -> a a\n", @@ -138,7 +139,7 @@ "id": "5ad45f13-4cd1-4a49-b614-e8a3baf75176", "metadata": {}, "source": [ - "### Grafting Heuristic" + "### Token Proposal" ] }, { @@ -165,12 +166,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e8659235-6e35-47f7-bc2e-082e7dab9665", + "execution_count": 4, + "id": "ab123080-e6fa-4882-a828-b25b04c595d5", + "metadata": {}, + "outputs": [], + "source": [ + "llm = MockLLM(B, EOS)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cc56fb43-620e-4db7-99ee-0b223cb8bc01", "metadata": {}, "outputs": [], "source": [ - "graft = CharAlignedCFGLM(p, B, EOS)" + "graft = TokenProposal(guide=p, llm=llm)" ] }, { @@ -183,30 +194,571 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d1d5c923-ef64-49f1-9201-5ee2c1ea2890", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_table(\n", - " [[p.cfg.language(100).project(ϕ), L_PB, generation_tree(graft).D, PL]],\n", - " headings=['target', 'pfst', 'grafting-heuristic', 'composition'],\n", + " [[p.cfg.language(100).project(ϕ).sort(), L_PB.sort(), generation_tree(graft).D.sort(), PL.sort()]],\n", + " headings=['target', 'pfst', 'grafting-heuristic', 'wfst'],\n", ")" ] }, + { + "cell_type": "markdown", + "id": "67a77d28-64a7-4498-ad03-fe13b0c1ab92", + "metadata": {}, + "source": [ + "### Character Proposal" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "99a2673c-8cbc-4079-b4b6-62882cfcf0d7", + "metadata": {}, + "outputs": [], + "source": [ + "from genparse.inference import TraceSWOR" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3fdf231a-8ab4-41d6-8a08-f9d91b7f5a05", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mass before: 1.0\n", + "a 0.55\n", + "mass before: 0.55\n", + "b 0.055\n", + "mass before: 0.055\n", + "b 0.005\n", + "mass before: 0.005\n", + "a 0\n" + ] + } + ], + "source": [ + "trace = TraceSWOR()\n", + "while trace.root.mass > 0:\n", + " with trace:\n", + " print('mass before:', trace.root.mass)\n", + " out = trace({'a': 0.5, 'b': 0.5})\n", + " if out == 'a':\n", + " out = trace({'a': 0.9, 'b': 0.1})\n", + " else: \n", + " out = trace({'a': 0.01, 'b': 0.99})\n", + " print(out, trace.root.mass)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f2c18869-02b5-414c-b756-4920a206ee9c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "0.5\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "a/0.5\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "0.5\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "b/0.5\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "0.45\n", + "\n", + "\n", + "\n", + "1->5\n", + "\n", + "\n", + "a/0.9\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "0.05\n", + "\n", + "\n", + "\n", + "1->6\n", + "\n", + "\n", + "b/0.1\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "0.005\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "a/0.01\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "0.49\n", + "\n", + "\n", + "\n", + "2->4\n", + "\n", + "\n", + "b/0.99\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trace" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e1b7c948-f0e2-4212-a39b-7a98b51d176d", + "metadata": {}, + "outputs": [], + "source": [ + "c_proposal = CharacterProposal(guide=p, llm=llm)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "adc78a36-d57c-4029-8dc2-3b2a3bc54ea6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('aaa', 0.06640625)\n", + "('aaa', 0.009684244791666668)\n", + "('aaa', 0.0030263264973958335)\n", + "('aaa', 0.020751953125)\n", + "('aa', 0.0726318359375)\n", + "('aa', 0.232421875)\n", + "('a', 0.33203125)\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "a/1\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1->2\n", + "\n", + "\n", + "a/1\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "a/1\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "0.47\n", + "\n", + "\n", + "\n", + "3->4\n", + "\n", + "\n", + "a/0.47\n", + "\n", + "\n", + "\n", + "5\n", + 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"trace = TraceSWOR()\n", + "while trace.root.mass > 0:\n", + " with trace:\n", + " out = c_proposal.sample('', draw=trace)\n", + " print(out)\n", + "trace" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "26dece5b-10e6-4aa0-9f86-6daafbb33adb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('a', 0.3125)\n", + "('aa', 0.3125)\n", + "('▪', 0.625)\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "0.1\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "▪/0.1\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "0.9\n", + "\n", + "\n", + "\n", + "0->2\n", + "\n", + "\n", + "a/0.9\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "0.1\n", + "\n", + "\n", + "\n", + "1->6\n", + "\n", + "\n", + "▪/1\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "0.9\n", + "\n", + "\n", + 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particle info when verbosity>0 --- genparse/inference.py | 46 +++++++++++++++++++++++++++++++++++++------ genparse/steer.py | 2 +- 2 files changed, 41 insertions(+), 7 deletions(-) diff --git a/genparse/inference.py b/genparse/inference.py index 4cee5701..c602bb51 100644 --- a/genparse/inference.py +++ b/genparse/inference.py @@ -63,12 +63,17 @@ async def smc_steer(model, n_particles, n_beam): Returns: particles (list[hfppl.modeling.Model]): The completed particles after inference. """ + verbosity = model.verbosity if hasattr(model, 'verbosity') else 0 + # Create n_particles copies of the model particles = [copy.deepcopy(model) for _ in range(n_particles)] for particle in particles: particle.start() + if verbosity > 0: + step_num = 1 + while any(map(lambda p: not p.done_stepping(), particles)): # Count the number of finished particles n_finished = sum(map(lambda p: p.done_stepping(), particles)) @@ -91,22 +96,48 @@ async def smc_steer(model, n_particles, n_beam): ) # Use optimal resampling to resample - W = np.array([p.weight for p in super_particles]) - W_tot = logsumexp(W) - W_normalized = softmax(W) - det_indices, stoch_indices, c = resample_optimal(W_normalized, n_particles) + weights = np.array([p.weight for p in super_particles]) + total_weight = logsumexp(weights) + normalized_weights = softmax(weights) + det_indices, stoch_indices, c = resample_optimal(normalized_weights, n_particles) + + if verbosity > 0: + for i, p in enumerate(particles): + print( + f'├ Particle {i:3d} (weight {p.weight:.4f}). `{p.context[-1]}` : {p}' + ) + for i, p in enumerate(super_particles): + print( + f'│├ Super-particle {i:3d} (weight {p.weight:.4f}). `{p.context[-1]}` : {p}' + ) + particles = [ super_particles[i] for i in np.concatenate((det_indices, stoch_indices)) ] + # For deterministic particles: w = w * N/N' for i in det_indices: super_particles[i].weight += np.log(n_particles) - np.log(n_total) # For stochastic particles: w = 1/c * total sum(stoch weights) / num_stoch = sum(stoch weights / total) / num_stoch * total * N/M for i in stoch_indices: super_particles[i].weight = ( - W_tot - np.log(c) + np.log(n_particles) - np.log(n_total) + total_weight - np.log(c) + np.log(n_particles) - np.log(n_total) ) + if verbosity > 0: + print( + '│└ ' f'resample_optimal: det={det_indices}, stoch={stoch_indices}, c={c}' + ) + for i, p in enumerate(particles): + print( + f'├ Particle {i:3d} (weight {p.weight:.4f}). `{p.context[-1]}` : {p}' + ) + avg_weight = logsumexp(np.array([p.weight for p in particles])) - np.log( + n_particles + ) + print(f'└╼ Step {step_num:3d} average weight: {avg_weight:.4f}') + step_num += 1 + # Return the particles return particles @@ -174,6 +205,8 @@ async def smc_standard(model, n_particles, ess_threshold=0.5): particles (list[hfppl.modeling.Model]): The completed particles after inference. """ verbosity = model.verbosity if hasattr(model, 'verbosity') else 0 + + # Create n_particles copies of the model particles = [copy.deepcopy(model) for _ in range(n_particles)] for particle in particles: @@ -199,7 +232,6 @@ async def smc_standard(model, n_particles, ess_threshold=0.5): f'├ Particle {i:3d} (weight {p.weight:.4f}). `{p.context[-1]}` : {p}' ) avg_weight = total_weight - np.log(n_particles) - print(f'│ Step {step_num:3d} average weight: {avg_weight:.4f}') step_num += 1 # Resample if necessary @@ -244,6 +276,8 @@ async def smc_standard_record(model, n_particles, ess_threshold=0.5, return_reco record (SMCRecord): Information about inference run history. """ verbosity = model.verbosity if hasattr(model, 'verbosity') else 0 + + # Create n_particles copies of the model particles = [copy.deepcopy(model) for _ in range(n_particles)] for particle in particles: diff --git a/genparse/steer.py b/genparse/steer.py index b2500727..49178374 100644 --- a/genparse/steer.py +++ b/genparse/steer.py @@ -467,7 +467,7 @@ def run_inference( if method == 'smc-steer': assert n_beam is not None if return_record: - raise Warning('Record not yet implemented for smc-steer') + warnings.warn('Record not yet implemented for smc-steer') particles = asyncio.run( smc_steer(model, n_particles=n_particles, n_beam=n_beam) ) From 5e71fed3b6a9e624759c99be7f54dcad73b9c6c8 Mon Sep 17 00:00:00 2001 From: Jacob Louis Hoover Date: Wed, 19 Jun 2024 10:14:34 -0400 Subject: [PATCH 07/35] make smc-steer print out particle info when verbosity>0 --- genparse/inference.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/genparse/inference.py b/genparse/inference.py index c602bb51..407086ec 100644 --- a/genparse/inference.py +++ b/genparse/inference.py @@ -123,6 +123,16 @@ async def smc_steer(model, n_particles, n_beam): super_particles[i].weight = ( total_weight - np.log(c) + np.log(n_particles) - np.log(n_total) ) + + if verbosity > 0: + print('│└ 'f'resample_optimal: det={det_indices}, stoch={stoch_indices}, c={c}') + for i, p in enumerate(particles): + print( + f'├ Particle {i:3d} (weight {p.weight:.4f}). `{p.context[-1]}` : {p}' + ) + avg_weight = logsumexp(np.array([p.weight for p in particles])) - np.log(n_particles) + print(f'└╼ Step {step_num:3d} average weight: {avg_weight:.4f}') + step_num += 1 if verbosity > 0: print( From b283e08a3e91aceea1affeff23dad9b085b8c027 Mon Sep 17 00:00:00 2001 From: Tim Vieira Date: Wed, 19 Jun 2024 14:39:15 -0400 Subject: [PATCH 08/35] tidying up --- .coveragerc | 3 +- Makefile | 2 +- genparse/experimental/{LL(k).py => LL.py} | 0 notes/Character-at-a-Time.ipynb | 1642 --------------------- notes/original_prefix_grammar.py | 76 - 5 files changed, 3 insertions(+), 1720 deletions(-) rename genparse/experimental/{LL(k).py => LL.py} (100%) delete mode 100644 notes/Character-at-a-Time.ipynb delete mode 100644 notes/original_prefix_grammar.py diff --git a/.coveragerc b/.coveragerc index c91a3350..70d5d706 100644 --- a/.coveragerc +++ b/.coveragerc @@ -2,7 +2,8 @@ [run] branch = True -omit = +include = + genparse/* [report] # Regexes for lines to exclude from consideration diff --git a/Makefile b/Makefile index b6dc5ffe..79b620da 100644 --- a/Makefile +++ b/Makefile @@ -44,4 +44,4 @@ pytest : env html/coverage/index.html html/coverage/index.html : html/pytest/report.html @coverage html -d $(@D) html/pytest/report.html : $(SRC_FILES) $(TEST_FILES) - @coverage run --branch -m pytest --html=$@ --self-contained-html $(TEST)/ $(NAME)/ + @coverage run --branch -m pytest --html=$@ --self-contained-html $(SRC_FILES) $(TEST_FILES) diff --git a/genparse/experimental/LL(k).py b/genparse/experimental/LL.py similarity index 100% rename from genparse/experimental/LL(k).py rename to genparse/experimental/LL.py diff --git a/notes/Character-at-a-Time.ipynb b/notes/Character-at-a-Time.ipynb deleted file mode 100644 index a4f6391a..00000000 --- a/notes/Character-at-a-Time.ipynb +++ /dev/null @@ -1,1642 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "21cf5561-f02d-4229-9d3d-b8326bf292b2", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "d5ca93c9-4d9d-4bb1-b87f-fefc8d609d7c", - "metadata": {}, - "outputs": [], - "source": [ - "from genparse import CFGLM\n", - "from genparse.cfglm import locally_normalize, EOS\n", - "from genparse.align.trie import TokenTrieApproximation\n", - "from genparse.lm import GreedilyTokenizedLLM\n", - "from genparse.util import LarkStuff\n", - "from genparse.inference import TraceSWOR" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c149b3b0-5388-48ab-9c28-1f853bf8f12e", - "metadata": {}, - "outputs": [], - "source": [ - "llm = GreedilyTokenizedLLM('gpt2')" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4757709c-95b5-4af8-8dea-2c78b2976456", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
keyvalue
 John
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 L
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 J
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 James
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" - ], - "text/plain": [ - "{' John': 0.16005247146281407, ' L': 0.11619228665146589, ' J': 0.10705686478148731, ' K': 0.1059574421280044, ' James': 0.10455176466452001, ' Michael': 0.08868320379871576, ' David': 0.08729231356700123, ' Tom': 0.07885149745865802, ' T': 0.07649287560150769, ' Jack': 0.07486927988582563}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prompt = 'Hello my name is'\n", - "pp = llm.p_next(prompt, 10).normalize()\n", - "pp" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9b8246ab-8a17-4fb8-9d94-3b393441bc7d", - "metadata": {}, - "outputs": [], - "source": [ - "# pp = llm.p_next('Noam Chomsky famously wrote, \"', 10)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e66bb79b-2d2b-4556-82de-13e972cd7d32", - "metadata": {}, - "outputs": [], - "source": [ - "# NextTokenTrie(pp, '<|endoftext|>').root.graphviz(fmt_node=lambda x: f'{x._mass:.3g}', fmt_edge=lambda i,a,j: repr(a))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13d460ba-90d9-4a09-897c-c6818b1f2377", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7eec5a3c-c5b0-4f71-ab08-f6e1fbed829c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c22f5e34-a005-4ea2-90c0-e00c99b2c576", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a525282f-4de5-4dad-a9a1-71ac5996908e", - "metadata": {}, - "outputs": [], - "source": [ - "# have = {x for x in llm._decode if len(x) == 1}\n", - "# want = {y for x in llm._decode for y in x}" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "07fa1acc-c8e8-4b50-9046-11f3b37b7129", - "metadata": {}, - "outputs": [], - "source": [ - "# want - have" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "eaa4a31e-421c-4068-a545-7853af9f6ceb", - "metadata": {}, - "outputs": [], - "source": [ - "# create a character-level trie for the next token given the prompt\n", - "# NextTokenTrie(pp).show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "a5bcbd68-049e-46cf-acc5-01af1d6b670d", - "metadata": {}, - "outputs": [], - "source": [ - "pcfg = CFGLM(\n", - " locally_normalize(\n", - " LarkStuff(\n", - " r\"\"\"\n", - "\n", - "start: /[ ]*(|Mr\\.[ ]|Dr\\.[ ])Tim(othy)?[ ](|Fabbri[ ])Vieira(|,[ ]Ph\\.D\\.)/\n", - "\n", - "\"\"\"\n", - " ).char_cfg(0.99),\n", - " tol=1e-100,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "0c2047f9-9102-47b9-8ccb-251c9618a34d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "' Tim Fabbri Vieira'\n" - ] - } - ], - "source": [ - "print(repr(''.join(pcfg.sample())))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a247b30-5798-4c18-b0cd-1858791be4eb", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "12b97356-3175-46f4-a3b4-69c3acc2005c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------------------------------\n", - "\u001b[0;36m D\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mr\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m.\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m T\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mim\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m F\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mabb\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mri\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m \u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mV\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mie\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mira\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m,\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m Ph\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m.\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36mD\u001b[0m\u001b[0;35m|\u001b[0m\u001b[0;36m.\u001b[0m\u001b[0;35m|\u001b[0m\n", - " Dr. Tim Fabbri Vieira, Ph.D.\n" - ] - } - ], - "source": [ - "token_trie_approx = TokenTrieApproximation(llm, pcfg)\n", - "tracer = TraceSWOR()\n", - "for _ in range(1):\n", - " with tracer:\n", - " print('----------------------------------')\n", - " ys = token_trie_approx.sample(prompt, max_tokens=50, draw=tracer, verbosity=1)\n", - " print(ys)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "ace39a45-6410-46b7-b5bf-d464ceb36d4c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "%3\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "1\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - "\n", - "' '\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - "7.1e-05\n", - "\n", - "\n", - "\n", - "0->2\n", - "\n", - "\n", - "'D'\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - "0.0001\n", - "\n", - "\n", - "\n", - "0->3\n", - "\n", - "\n", - "'M'\n", - "\n", - "\n", - 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"\n", - "0.0212\n", - "\n", - "\n", - "\n", - "94->95\n", - "\n", - "\n", - "'▪'\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tracer.root.graphviz(\n", - " fmt_node=lambda x: f'{x._mass:.3g}', fmt_edge=lambda i, a, j: repr(a)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c992994d-13cc-45cf-b738-e6ea377a7500", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "5d3819b9-fbaa-4347-ba30-04dfe129c265", - "metadata": {}, - "outputs": [], - "source": [ - "fruit = CFGLM(\n", - " locally_normalize(\n", - " LarkStuff(\n", - " r\"\"\"\n", - "start: (|\" \") sentence\n", - "\n", - "sentence: noun verb noun \n", - " | noun verb \"like\" noun \n", - "\n", - "noun: det* adj* NOUN\n", - "verb: VERB\n", - "adj: ADJ\n", - "det: \"a\" | \"the\"\n", - "\n", - "NOUN: \"flies\" | \"banana\" | \"fruit\"\n", - "VERB: \"like\" | \"flies\"\n", - "ADJ: \"fruit\"\n", - "\n", - "\"\"\"\n", - " ).char_cfg(0.99, ignore='[ ]?'),\n", - " tol=1e-100,\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "3af2eb3e-6512-403a-a4bb-72f66b48d972", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "' fruit banana flies fruit '" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "''.join(fruit.sample())" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "a3ca6b23-4c3c-430e-a229-544825195aca", - "metadata": {}, - "outputs": [], - "source": [ - "# fruit.cfg.language(20).project(lambda x: ''.join(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "e41f9ceb-4577-4594-98e0-d7177a91f231", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2.2463994597622037e-05" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fruit('fruit flies like a banana ' + EOS)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "605aefd4-bba3-4a72-89f9-ec119bd5d86a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "----------------------------------\n", - "1.0\n", - " a the a banana likethe a banana \n" - ] - } - ], - "source": [ - "prompt = 'The following is a favorite sentence among linguists:'\n", - "token_trie_approx = TokenTrieApproximation(llm, fruit)\n", - "tracer = TraceSWOR()\n", - "for _ in range(1):\n", - " with tracer:\n", - " print('----------------------------------')\n", - " ys = token_trie_approx.sample(prompt, max_tokens=50, draw=tracer)\n", - " print(ys)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b19a7861-45ab-40c5-97ad-b2bff3e88139", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notes/original_prefix_grammar.py b/notes/original_prefix_grammar.py deleted file mode 100644 index be60257f..00000000 --- a/notes/original_prefix_grammar.py +++ /dev/null @@ -1,76 +0,0 @@ -from genparse import CFG - - -# TODO: replace this code with the transduction version! -class PrefixGrammar(CFG): - """ - Left-derivative transformation returns a grammar that computes all left - derivatives when it is intersected with a straight-line automaton accepting - a given input string. - """ - - def __init__(self, parent): - self.parent = parent - other = self._other - free = self._free - top = self._top - super().__init__( - S=top(parent.S), - V=parent.V, - R=parent.R, - ) - - # Our construction for `other` assumes that there are new empty strings - # to get those back we add one more kind of item that unions them. - # - # TODO: can we merge `top` with `other`? - for x in parent.N: - self.add(self.R.one, top(x), free(x)) - self.add(self.R.one, top(x), other(x)) - - # keep all of the original rules - for r in parent: - self.add(r.w, r.head, *r.body) - - # invisible suffix. These are empty "future strings". The rules add - # 'free' rules with the exact same structure, but different base cases, - # as they generate empty strings only - for x in parent.V: - self.add(self.R.one, free(x)) # generates the empty string - for r in parent: - self.add(r.w, free(r.head), *(free(z) for z in r.body)) - - # The `other` items (better name pending) are possibly incomplete items - # that all nonempty prefixes of their base nonterminal's language. Top - # is the same, but it includes the empty string. - # - # visible prefix - Below, we carefully move the `other`-cursor along - # each rule body.. The `other` are such that they have an `other`-spine - # that separates the /visible/ prefix from the /invisible/ suffix. - for x in parent.V: - self.add(self.R.one, other(x), x) # generates the usual string - for r in parent: - for k in range(len(r.body)): - self.add( - r.w, - other(r.head), - *r.body[:k], - other(r.body[k]), - *(free(z) for z in r.body[k + 1 :]), - ) - - def spawn(self, *, R=None, S=None, V=None): # override or else we will spawn - return CFG( - R=self.R if R is None else R, - S=self.S if S is None else S, - V=set(self.V) if V is None else V, - ) - - def _other(self, x): - return self.parent.gensym(f'{x}⚡') - - def _free(self, x): - return self.parent.gensym(f'{x}🔥') - - def _top(self, x): - return self.parent.gensym(f'#{x}') From ee1ff339db270b0553b2e684e3a87dc4269fea1d Mon Sep 17 00:00:00 2001 From: Tim Vieira Date: Wed, 19 Jun 2024 16:47:50 -0400 Subject: [PATCH 09/35] misc cleanups --- genparse/cfglm.py | 47 +- genparse/chart.py | 5 +- genparse/lm.py | 59 +- genparse/steer.py | 118 +- genparse/util.py | 161 +- notes/hfppl_jac.ipynb | 17920 +++++------------------------ tests/test_character_proposal.py | 63 +- 7 files changed, 2760 insertions(+), 15613 deletions(-) diff --git a/genparse/cfglm.py b/genparse/cfglm.py index 733b0a2d..3c7a83c2 100644 --- a/genparse/cfglm.py +++ b/genparse/cfglm.py @@ -41,30 +41,30 @@ def locally_normalize(self, **kwargs): return new -class BoolMaskCFGLM(LM): - "LM-like interface for Boolean-masking CFG models; uses CKY for inference." +# class BoolMaskCFGLM(LM): +# "LM-like interface for Boolean-masking CFG models; uses CKY for inference." +# +# def __init__(self, cfg): +# if EOS not in cfg.V: +# cfg = add_EOS(cfg) +# if cfg.R != Boolean: +# cfg = cfg.map_values(lambda x: Boolean(x > 0), Boolean) +# self.model = CFGLM(cfg) +# super().__init__(eos=self.model.eos, V=self.model.V) +# +# def p_next(self, context): +# p = self.model.p_next(context).trim() +# return Float.chart({w: 1 for w in p}) +# +# def __call__(self, context): +# assert context[-1] == EOS +# return float(self.model(context) != Boolean.zero) +# +# def clear_cache(self): +# self.model.clear_cache() - def __init__(self, cfg): - if EOS not in cfg.V: - cfg = add_EOS(cfg) - if cfg.R != Boolean: - cfg = cfg.map_values(lambda x: Boolean(x > 0), Boolean) - self.model = CFGLM(cfg) - super().__init__(eos=self.model.eos, V=self.model.V) - def p_next(self, context): - p = self.model.p_next(context).trim() - return Float.chart({w: 1 for w in p}) - - def __call__(self, context): - assert context[-1] == EOS - return float(self.model(context) != Boolean.zero) - - def clear_cache(self): - self.model.clear_cache() - - -class EarleyBoolMaskCFGLM(LM): +class BoolMaskCFGLM(LM): "LM-like interface for Boolean-masking CFG models; uses Earley's algorithm for inference." def __init__(self, cfg): @@ -89,6 +89,9 @@ def clear_cache(self): self.model.clear_cache() +EarleyBoolMaskCFGLM = BoolMaskCFGLM + + class CFGLM(LM): """ Probabilistic Context-Free Grammar Language Model. diff --git a/genparse/chart.py b/genparse/chart.py index 4b884904..81d2365e 100644 --- a/genparse/chart.py +++ b/genparse/chart.py @@ -102,7 +102,10 @@ def argmin(self): return min(self, key=self.__getitem__) def top(self, k): - return {k: self[k] for k in sorted(self, key=self.__getitem__, reverse=True)[:k]} + return Chart( + self.semiring, + {k: self[k] for k in sorted(self, key=self.__getitem__, reverse=True)[:k]}, + ) def max(self): return max(self.values()) diff --git a/genparse/lm.py b/genparse/lm.py index 08886b3d..c5e23851 100644 --- a/genparse/lm.py +++ b/genparse/lm.py @@ -29,6 +29,7 @@ def __init__(self, V, eos): self.eos = eos self.V = V + # TODO: give a default implementation in terms of p_next def __call__(self, ys): "Compute the probability of a complete string." raise NotImplementedError() @@ -129,31 +130,31 @@ def p_next(self, input_ids): return probs[0, :] # return the conditional distribution of just the last token -class NoCacheLLM(LM): - """ - This is a simple class that wraps HuggingFace transformers. - """ - - def __init__(self, model): - self.model = model - self.model.eval() # Set the model in "evaluation mode" - - # TODO: Add vocabulary and EOS - - # super().__init__(set(range()), eos=eos) - - def __call__(self, input_ids): - raise NotImplementedError() - - def p_next(self, input_ids): - if isinstance(input_ids, (tuple, list)): - input_ids = torch.LongTensor([input_ids]) - with torch.no_grad(): - outputs = self.model(input_ids=input_ids, labels=input_ids) - lprobs = torch.nn.functional.softmax(outputs.logits, dim=-1) - return lprobs[ - 0, -1, : - ] # return the conditional distribution of just the last token +# class NoCacheLLM(LM): +# """ +# This is a simple class that wraps HuggingFace transformers. +# """ +# +# def __init__(self, model): +# self.model = model +# self.model.eval() # Set the model in "evaluation mode" +# +# # TODO: Add vocabulary and EOS +# +# # super().__init__(set(range()), eos=eos) +# +# def __call__(self, input_ids): +# raise NotImplementedError() +# +# def p_next(self, input_ids): +# if isinstance(input_ids, (tuple, list)): +# input_ids = torch.LongTensor([input_ids]) +# with torch.no_grad(): +# outputs = self.model(input_ids=input_ids, labels=input_ids) +# lprobs = torch.nn.functional.softmax(outputs.logits, dim=-1) +# return lprobs[ +# 0, -1, : +# ] # return the conditional distribution of just the last token class GreedilyTokenizedLLM(LM): @@ -243,8 +244,8 @@ def from_name(cls, name, batch_size): from hfppl import CachedCausalLM return cls( - tokenizer=AutoTokenizer.from_pretrained(name, use_fast=True), model=CachedCausalLM.from_pretrained(name, load_in_8bit=True), + tokenizer=AutoTokenizer.from_pretrained(name, use_fast=True), batch_size=batch_size, ) @@ -338,9 +339,9 @@ def __init__(self, V, eos, _p=None): def p_next(self, _): return LazyProb(self._p, self._encode, self._decode) - def __call__(self, x): - assert x[-1] == self.eos - return (1 / len(self.V)) ** len(x) + # def __call__(self, x): + # assert x[-1] == self.eos + # return (1 / len(self.V)) ** len(x) def clear_cache(self): pass diff --git a/genparse/steer.py b/genparse/steer.py index 49178374..b074fbfd 100644 --- a/genparse/steer.py +++ b/genparse/steer.py @@ -23,23 +23,7 @@ ) from genparse.lm import LM from genparse.semiring import Float -from genparse.util import format_table, normalize - -# ____________________________________________________________________________________ -# - - -def set_seed(seed): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - transformers.set_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) - - -# ____________________________________________________________________________________ -# +from genparse.util import format_table, normalize, set_seed class BruteForceGlobalProductOfExperts: @@ -78,49 +62,45 @@ def _repr_html_(self): return format_table([[self.D, self.tracer]]) -# ____________________________________________________________________________________ +# class LocalProduct(LM): +# """This class implements a *local* product of experts, an LM that is derived by +# multiplying the conditional distributions of each token in a pair of +# token-synchronized LM. # - - -class LocalProduct(LM): - """This class implements a *local* product of experts, an LM that is derived by - multiplying the conditional distributions of each token in a pair of - token-synchronized LM. - - Typically, `LocalProduct` is a baseline method or a proposal distribution - for the *global* product of experts. - - [Some people call LocalProduct the "locally optimal proposal distribution" - - what does it actually optimize?] - - """ - - def __init__(self, lm1, lm2): - self.lm1 = lm1 - self.lm2 = lm2 - assert lm1.V == lm2.V - assert lm1.eos == lm2.eos - super().__init__(V=lm1.V, eos=lm1.eos) - - def __call__(self, ys): - assert ys[-1] == self.eos - p = 1 - for t in range(len(ys)): - p *= self.p_next(ys[:t])[ys[t]] - return p - - def p_next(self, ys): - p1 = self.lm1.p_next(ys) - p2 = self.lm2.p_next(ys) - - # TODO: p_next should already be normalized! Skipping the normalization - # below would allow energy-based models. - p1 = normalize(p1) - p2 = normalize(p2) - - # Below, we could alternatively use p2's support; any `k` that's not in - # both must have probability zero. - return (p1 * p2).normalize() +# Typically, `LocalProduct` is a baseline method or a proposal distribution +# for the *global* product of experts. +# +# [Some people call LocalProduct the "locally optimal proposal distribution" - +# what does it actually optimize?] +# +# """ +# +# def __init__(self, lm1, lm2): +# self.lm1 = lm1 +# self.lm2 = lm2 +# assert lm1.V == lm2.V +# assert lm1.eos == lm2.eos +# super().__init__(V=lm1.V, eos=lm1.eos) +# +# def __call__(self, ys): +# assert ys[-1] == self.eos +# p = 1 +# for t in range(len(ys)): +# p *= self.p_next(ys[:t])[ys[t]] +# return p +# +# def p_next(self, ys): +# p1 = self.lm1.p_next(ys) +# p2 = self.lm2.p_next(ys) +# +# # TODO: p_next should already be normalized! Skipping the normalization +# # below would allow energy-based models. +# p1 = normalize(p1) +# p2 = normalize(p2) +# +# # Below, we could alternatively use p2's support; any `k` that's not in +# # both must have probability zero. +# return (p1 * p2).normalize() # _______________________________________________________________________________ @@ -488,27 +468,29 @@ def run_inference( else: raise ValueError(f'Unknown inference method: {method}.') - return ParticleApproximation(particles), record + return ParticleApproximation(particles, record=record) class ParticleApproximation: - def __init__(self, particles): + def __init__(self, particles, record=None): self.particles = particles self.log_weights = [p.weight for p in self.particles] self.log_ml = logsumexp(self.log_weights) - np.log(len(self.log_weights)) - self._compute_posterior() + self.record = record + + posterior = Float.chart() + for p in self.particles: + posterior[''.join(p.context)] += np.exp(p.weight) + self.posterior = posterior.normalize() def __iter__(self): return iter(self.particles) - def _compute_posterior(self): - self.posterior = Float.chart() - for p in self.particles: - self.posterior[str(p)] += np.exp(p.weight) - self.posterior = self.posterior.normalize() - def sample(self, n=None, draw=sample_dict): if n is None: return draw(self.posterior) else: return [draw(self.posterior) for _ in range(n)] + + def __repr__(self): + return repr(self.posterior) diff --git a/genparse/util.py b/genparse/util.py index e4ea0b09..e5cc1fde 100644 --- a/genparse/util.py +++ b/genparse/util.py @@ -4,6 +4,20 @@ from IPython.display import HTML, display +import numpy as np +import random +import torch +import transformers + + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + transformers.set_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + class hf_tokenizer: def __init__(self, name='gpt2', **kwargs): @@ -436,150 +450,3 @@ def fmt(x): def display_table(*args, **kwargs): return display(HTML(format_table(*args, **kwargs))) - - -class Node: - """ - This class represents a node in the directed acyclic word graph (DAWG). It - has a list of edges to other nodes. It has functions for testing whether it - is equivalent to another node. Nodes are equivalent if they have identical - edges, and each identical edge leads to identical states. The __hash__ and - __eq__ functions allow it to be used as a key in a python dictionary. - """ - - NextId = 0 - - def __init__(self): - self.id = Node.NextId - Node.NextId += 1 - self.final = False - self.edges = {} - - def __getitem__(self, x): - return self.edges[x] - - def __setitem__(self, x, v): - self.edges[x] = v - - def __str__(self): - arr = [] - if self.final: - arr.append('1') - else: - arr.append('0') - for label, node in self.edges.items(): - arr.append(label) - arr.append(str(node.id)) - return '_'.join(arr) - - def __hash__(self): - return hash(str(self)) - - def __eq__(self, other): - return str(self) == str(other) - - -class DAWG: - """ - Directed acyclic word graph (DAWG). - - Original implementation by Steve Hanov, 2011. - http://stevehanov.ca/blog/?id=115 - """ - - def __init__(self): - self.root = Node() - - @classmethod - def build(cls, words): - d = DAWG() - - # Here is a list of nodes that have not been checked for duplication. - uncheckedNodes = [] - # Here is a list of unique nodes that have been checked for - # duplication. - minimizedNodes = {} - - def _minimize(downTo): - # proceed from the leaf up to a certain point - for i in reversed(range(downTo, len(uncheckedNodes))): - (parent, letter, child) = uncheckedNodes[i] - if child in minimizedNodes: - # replace the child with the previously encountered one - parent[letter] = minimizedNodes[child] - else: - # add the state to the minimized nodes. - minimizedNodes[child] = child - uncheckedNodes.pop() - - previousWord = '' - for word in sorted(words): - # assert previousWord <= word, "Words must be inserted in alphabetical order." - - # find common prefix between word and previous word - commonPrefix = common_prefix(previousWord, word) - - # Check the uncheckedNodes for redundant nodes, proceeding from last - # one down to the common prefix size. Then truncate the list at that - # point. - _minimize(commonPrefix) - - # add the suffix, starting from the correct node mid-way through the - # graph - if len(uncheckedNodes) == 0: - node = d.root - else: - node = uncheckedNodes[-1][2] - - for letter in word[commonPrefix:]: - nextNode = Node() - node[letter] = nextNode - uncheckedNodes.append((node, letter, nextNode)) - node = nextNode - - node.final = True - previousWord = word - - _minimize(0) - - return d - - def lookup(self, word): - node = self.root - for letter in word: - if letter not in node.edges: - return False - node = node.edges[letter] - return node.final - - -def common_prefix(x, y): - p = 0 - for i in range(min(len(x), len(y))): - if x[i] != y[i]: - break - p += 1 - return p - - -def dawg_wfsa_from_strings(strings): - from genparse import WFSA, Float - - d = DAWG.build(strings) - - dawg = WFSA(Float) - dawg.set_I(d.root.id, 1) - visited = set() - - def traverse(x): - assert isinstance(x, Node) - if x in visited: - return - if x.final: - dawg.set_F(x.id, 1) - for a, y in x.edges.items(): - dawg.set_arc(x.id, a, y.id, 1) - traverse(y) - - traverse(d.root) - return dawg diff --git a/notes/hfppl_jac.ipynb b/notes/hfppl_jac.ipynb index 7b568eb1..360bf13e 100644 --- a/notes/hfppl_jac.ipynb +++ b/notes/hfppl_jac.ipynb @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -151,11 +151,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "particle_approx, record = sampler.run_inference(\n", + "particle_approx = sampler.run_inference(\n", " prompt=prompt,\n", " proposal=proposal,\n", " method='smc-standard',\n", @@ -168,19 +168,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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Particle 0; Weight = -30.971712
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669616199219999323103812
Step 41; Avg weight = -9.792840
Particle 0; Weight = -30.971712
", - "Token: `79`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313
Step 43; Avg weight = -9.894529
Particle 0; Weight = -9.894334
", - "Token: `68`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431379
Step 44; Avg weight = -9.910677
Particle 0; Weight = -9.900107
", - "Token: `34`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137968
Step 45; Avg weight = -9.921427
Particle 0; Weight = -9.905303
", - "Token: `157`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834
Step 46; Avg weight = -9.999674
Particle 0; Weight = -9.911647
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157
Step 47; Avg weight = -10.018553
Particle 0; Weight = -9.917086
", - "Token: `58`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431379683415710
Step 48; Avg weight = -10.029880
Particle 0; Weight = -9.922265
", - "Token: `163`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137968341571058
Step 49; Avg weight = -10.149053
Particle 0; Weight = -9.927522
", - "Token: `34`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137968341571058163
Step 50; Avg weight = -10.160593
Particle 0; Weight = -9.933275
", - "Token: `136`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157105816334
Step 51; Avg weight = -10.174623
Particle 0; Weight = -9.939255
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157105816334136
Step 52; Avg weight = -10.181790
Particle 0; Weight = -9.941978
", - "Token: `11`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431379683415710581633413630
Step 53; Avg weight = -10.191848
Particle 0; Weight = -9.944682
", - "Token: `15`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137968341571058163341363011
Step 54; Avg weight = -10.225102
Particle 0; Weight = -10.183359
", - "Token: `486`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157105816334136301115
Step 55; Avg weight = -10.231485
Particle 0; Weight = -10.188106
", - "Token: `16`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157105816334136301115486
Step 56; Avg weight = -10.237751
Particle 0; Weight = -10.195175
", - "Token: `56`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431379683415710581633413630111548616
Step 57; Avg weight = -10.244302
Particle 0; Weight = -10.199820
", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137968341571058163341363011154861656
Step 58; Avg weight = -10.249955
Particle 0; Weight = -10.206526
", - "Token: `58`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313796834157105816334136301115486165650
Step 59; Avg weight = -10.255388
Particle 0; Weight = -10.212554
", - "Token: `17`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431379683415710581633413630111548616565058
Step 60; Avg weight = -10.262210
Particle 0; Weight = -10.217025
" + "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 0; Weight = -0.760898
", + "Token: ` zip`
Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 0; Weight = -0.976939
", + "Token: `code`
Context: SELECT age, zip
Step 5; Avg weight = -1.283818
Particle 0; Weight = -0.977817
", + "Token: `,`
Context: SELECT age, zipcode
Step 6; Avg weight = -1.382515
Particle 0; Weight = -0.984795
", + "Token: ` vote`
Context: SELECT age, zipcode,
Step 7; Avg weight = -1.593709
Particle 0; Weight = -1.504912
", + "Token: ` FROM`
Context: SELECT age, zipcode, vote
Step 8; Avg weight = -1.701664
Particle 0; Weight = -1.834555
", + "Token: ` data`
Context: SELECT age, zipcode, vote FROM
Step 9; Avg weight = -1.831096
Particle 0; Weight = -2.016027
", + "Token: ` ORDER`
Context: SELECT age, zipcode, vote FROM data
Step 10; Avg weight = -1.957496
Particle 0; Weight = -2.148296
", + "Token: ` BY`
Context: SELECT age, zipcode, vote FROM data ORDER
Step 11; Avg weight = -2.368902
Particle 0; Weight = -2.149252
", + "Token: ` age`
Context: SELECT age, zipcode, vote FROM data ORDER BY
Step 12; Avg weight = -2.817760
Particle 0; Weight = -2.261326
↳ resampled as particle 0", + "Token: ` BY`
Context: SELECT vote, zipcode, age FROM data ORDER
Step 12; Avg weight = -2.817760
Particle 1; Weight = -6.416387
↳ resampled as particle 0", + "Token: ` ASC`
Context: SELECT age, zipcode, vote FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 0; Weight = -3.476535
", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 0; Weight = -4.767722
", + "Token: `s`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC Step 15; Avg weight = -5.379061
Particle 0; Weight = -4.768406
", + "Token: `>`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 0; Weight = -4.771100
↳ resampled as particle 0", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 0; Weight = -7.571351
↳ resampled as particle 0", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 1; Weight = -9.179713
↳ resampled as particle 0", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 18; Avg weight = -8.748671
Particle 0; Weight = -7.068531
↳ resampled as particle 0", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 18; Avg weight = -8.748671
Particle 1; Weight = -7.068531
↳ resampled as particle 0", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 18; Avg weight = -8.748671
Particle 2; Weight = -15.534495
↳ resampled as particle 0", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 3; Weight = -10.709838
↳ resampled as particle 0", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 4; Weight = -10.709838
↳ resampled as particle 0", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 0; Weight = -8.748671
" ], "marker": { "color": "rgb(255.0, 0.0, 0.0)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, + 14.142135623730951, + 14.142135623730951, 14.876188905320037, - 15.651562248364147, - 15.463306612906164, - 15.554445252926628, - 15.21650837141921, - 15.281764212426436, - 15.674147576892272, - 17.356023449467067, - 21.10240551360585, - 14.613581125763352, - 12.376190929480302, - 8.937186926479125, - 11.511607219857938, - 13.38778307034682, - 0.34130378194558536, - 0.4241522577448174, - 4.255457489559425, - 4.848969368423761, - 4.41176553116367, - 2.611955699254139, - 2.823976375694531, - 6.329447544247099, - 0.14020795681481082, - 0.14226949130565145, - 0.15176794294509255, - 0.01207769141185093, - 0.00024671070254900034, - 0.0002544109592769661, - 0.0002578674337397394, - 0.00026957915511084527, - 0.00028961075805125804, - 0.0002918902040659, - 0.0002977349470875851, - 0.00032643688971621584, - 0.0003315882104788915, - 0.00035610755471250687, - 14.14350971627556, - 14.217077858263847, - 14.256609835412968, - 14.778477420246476, - 14.878130620509111, - 14.923934118873902, - 15.798638896151704, - 15.84441910756549, - 15.908328475680518, - 15.94371095408267, - 16.002443462396226, - 14.44040623872528, - 14.45221956860266, - 14.446419807749605, - 14.460197752962564, - 14.452586617332038, - 14.448280429421594, - 14.465279937331095 + 15.07074040392218, + 15.227773706709241, + 16.480190539871565, + 17.253557899826216, + 14.78417511452872, + 13.232990711121536, + 12.893112418204204, + 12.855336393785857, + 15.783789178052773, + 18.678550206858514, + 2.3392846099387974, + 12.525581649649281, + 8.849523267032996, + 19.19186439427444, + 19.642749756869918, + 10.998387715587024, + 4.92127331385489, + 32.7606822291635, + 32.7606822291635, + 0.4753266387207686, + 5.304604544335504, + 5.304604544335504, + 15.491603946203218 ] }, "mode": "markers+text", @@ -587,58 +689,28 @@ " SELECT", " age", ",", - " gender", + " zip", + "code", + ",", + " vote", " FROM", " data", - " WHERE", - " gender", - ">", - "90", - " <", - " ASC", - " ", - "/", - "s", - " GROUP", + " ORDER", " BY", " age", - " ORDER", " BY", - " <", - "/", + " ASC", + " ", - " ", - "▪", - "▪", - "▪", - "▪", - "▪", - "▪", "▪", + " ", "▪", "▪", "▪", - "79", - "68", - "34", - "157", - "10", - "58", - "163", - "34", - "136", - "30", - "11", - "15", - "486", - "16", - "56", - "50", - "58", - "17" + " ", + " ", + "▪" ], "type": "scatter", "x": [ @@ -654,50 +726,20 @@ 9, 10, 11, + 12, + 12, 13, 14, 15, 16, + 17, + 17, 18, - 19, - 21, - 22, - 23, - 24, - 25, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 18, + 18, + 18, + 18, + 19 ], "y": [ 0, @@ -713,48 +755,18 @@ 0, 0, 0, + 1, 0, 0, 0, 0, 0, + 1, 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, + 1, + 2, + 3, + 4, 0 ] }, @@ -764,142 +776,60 @@ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 1; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 1; Weight = -0.215239
", "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 1; Weight = -0.796165
", - "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.963722
Particle 1; Weight = -0.851550
", - "Token: ` zip`
Context: SELECT vote,
Step 4; Avg weight = -1.175300
Particle 1; Weight = -0.941903
", - "Token: `code`
Context: SELECT vote, zip
Step 5; Avg weight = -1.228179
Particle 1; Weight = -0.942447
", - "Token: `,`
Context: SELECT vote, zipcode
Step 6; Avg weight = -1.345798
Particle 1; Weight = -0.949013
", - "Token: ` age`
Context: SELECT vote, zipcode,
Step 7; Avg weight = -1.463050
Particle 1; Weight = -1.070736
", - "Token: ` FROM`
Context: SELECT vote, zipcode, age
Step 8; Avg weight = -1.561001
Particle 1; Weight = -1.109007
", - "Token: ` data`
Context: SELECT vote, zipcode, age FROM
Step 9; Avg weight = -1.799961
Particle 1; Weight = -1.266945
", - "Token: ` ORDER`
Context: SELECT vote, zipcode, age FROM data
Step 10; Avg weight = -2.215472
Particle 1; Weight = -1.344691
", - "Token: ` BY`
Context: SELECT vote, zipcode, age FROM data ORDER
Step 11; Avg weight = -2.579298
Particle 1; Weight = -1.345477
", - "Token: `/`
Context: SELECT age, gender FROM data WHERE gender>90 <
Step 12; Avg weight = -3.332867
Particle 0; Weight = -8.378636
↳ resampled as particle 1", - "Token: ` age`
Context: SELECT vote, zipcode, age FROM data ORDER BY
Step 12; Avg weight = -3.332867
Particle 1; Weight = -1.442531
↳ resampled as particle 1", - "Token: ` `
Context: SELECT age, gender FROM data ORDER BY age DESC
Step 12; Avg weight = -3.332867
Particle 2; Weight = -4.962143
↳ resampled as particle 1", - "Token: ` `
Context: SELECT age FROM data ORDER BY age ASC
Step 12; Avg weight = -3.332867
Particle 3; Weight = -7.316819
↳ resampled as particle 1", - "Token: ` ORDER`
Context: SELECT vote, age, zipcode, vote FROM data
Step 12; Avg weight = -3.332867
Particle 4; Weight = -2.567565
↳ resampled as particle 1", - "Token: `s`
Context: SELECT age, gender FROM data ORDER BY age ASC Step 12; Avg weight = -3.332867
Particle 5; Weight = -3.157753
↳ resampled as particle 1", - "Token: `20`
Context: SELECT age, zipcode FROM data WHERE vote < 50
Step 12; Avg weight = -3.332867
Particle 6; Weight = -3.752894
↳ resampled as particle 1", - "Token: ` DE`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 1; Weight = -3.952125
", - "Token: `S`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DE
Step 14; Avg weight = -4.248732
Particle 1; Weight = -13.323982
", - "Token: `C`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DES
Step 15; Avg weight = -4.755635
Particle 1; Weight = -13.327013
", - "Token: `00`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566
Step 18; Avg weight = -6.735819
Particle 1; Weight = -6.480502
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456600
Step 19; Avg weight = -7.171824
Particle 1; Weight = -7.719580
", - "Token: `1999`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456696161992
Step 21; Avg weight = -7.750867
Particle 1; Weight = -7.769044
", - "Token: `93`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566961619921999
Step 22; Avg weight = -8.015190
Particle 1; Weight = -7.836961
", - "Token: `23`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456696161992199993
Step 23; Avg weight = -8.109812
Particle 1; Weight = -7.911254
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669616199219999323
Step 24; Avg weight = -8.338520
Particle 1; Weight = -7.963479
", - "Token: `38`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566961619921999932310
Step 25; Avg weight = -8.503752
Particle 1; Weight = -8.021905
", - "Token: ` vote`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456696161992 GROUP BY age ORDER BY
Step 26; Avg weight = -8.627681
Particle 0; Weight = -12.100853
↳ resampled as particle 1", - "Token: `237`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698
Step 27; Avg weight = -8.744810
Particle 1; Weight = -8.695257
", - "Token: `37`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237
Step 28; Avg weight = -8.810626
Particle 1; Weight = -8.733265
", - "Token: `810`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737
Step 29; Avg weight = -8.849632
Particle 1; Weight = -8.764553
", - "Token: `82`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810
Step 30; Avg weight = -8.994838
Particle 1; Weight = -8.802022
", - "Token: `175`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082
Step 31; Avg weight = -9.029883
Particle 1; Weight = -8.874347
", - "Token: `53`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175
Step 32; Avg weight = -9.058807
Particle 1; Weight = -8.898452
", - "Token: `188`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553
Step 33; Avg weight = -9.120276
Particle 1; Weight = -9.046935
", - "Token: `996`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188
Step 34; Avg weight = -9.147265
Particle 1; Weight = -9.058498
", - "Token: `17`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996
Step 35; Avg weight = -9.236098
Particle 1; Weight = -9.084283
", - "Token: `63`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617
Step 36; Avg weight = -9.379450
Particle 1; Weight = -9.096053
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763
Step 37; Avg weight = -9.395129
Particle 1; Weight = -9.120140
", - "Token: `01`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313
Step 38; Avg weight = -9.434781
Particle 1; Weight = -9.132720
", - "Token: `376`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301
Step 39; Avg weight = -9.618847
Particle 1; Weight = -9.150677
", - "Token: `18`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376
Step 40; Avg weight = -9.650162
Particle 1; Weight = -9.161016
", - "Token: `43`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618
Step 41; Avg weight = -9.792840
Particle 1; Weight = -9.165574
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669616199219999323103812
Step 42; Avg weight = -9.884456
Particle 0; Weight = -30.971712
↳ resampled as particle 1", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843
Step 42; Avg weight = -9.884456
Particle 1; Weight = -9.178732
↳ resampled as particle 1", - "Token: `659`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810
Step 42; Avg weight = -9.884456
Particle 2; Weight = -9.432832
↳ resampled as particle 1", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451
Step 42; Avg weight = -9.884456
Particle 3; Weight = -22.208742
↳ resampled as particle 1", - "Token: `754`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313
Step 43; Avg weight = -9.894529
Particle 1; Weight = -9.894334
", - "Token: `349`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754
Step 44; Avg weight = -9.910677
Particle 1; Weight = -9.904194
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349
Step 45; Avg weight = -9.921427
Particle 1; Weight = -9.908629
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431375434970
Step 46; Avg weight = -9.999674
Particle 1; Weight = -9.912306
", - "Token: `65`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137543497099
Step 47; Avg weight = -10.018553
Particle 1; Weight = -9.918572
", - "Token: `759`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965
Step 48; Avg weight = -10.029880
Particle 1; Weight = -9.927224
", - "Token: `287`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965759
Step 49; Avg weight = -10.149053
Particle 1; Weight = -9.936199
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965759287
Step 50; Avg weight = -10.160593
Particle 1; Weight = -9.942010
", - "Token: `29`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431375434970996575928730
Step 51; Avg weight = -10.174623
Particle 1; Weight = -9.946303
", - "Token: `49`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137543497099657592873029
Step 52; Avg weight = -10.181790
Particle 1; Weight = -9.951286
", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965759287302949
Step 53; Avg weight = -10.191848
Particle 1; Weight = -9.956197
", - "Token: `58`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431375434970996575928730294950
Step 54; Avg weight = -10.225102
Particle 1; Weight = -9.962248
", - "Token: `17`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137543497099657592873029495058
Step 55; Avg weight = -10.231485
Particle 1; Weight = -9.967375
", - "Token: `586`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965759287302949505817
Step 56; Avg weight = -10.237751
Particle 1; Weight = -9.974160
", - "Token: `39`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313754349709965759287302949505817586
Step 57; Avg weight = -10.244302
Particle 1; Weight = -9.982221
", - "Token: `454`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431375434970996575928730294950581758639
Step 58; Avg weight = -10.249955
Particle 1; Weight = -9.985948
", - "Token: `27`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431375434970996575928730294950581758639454
Step 59; Avg weight = -10.255388
Particle 1; Weight = -9.992336
", - "Token: `28`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843137543497099657592873029495058175863945427
Step 60; Avg weight = -10.262210
Particle 1; Weight = -9.996363
" + "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.888091
Particle 1; Weight = -0.851550
", + "Token: ` z`
Context: SELECT vote,
Step 4; Avg weight = -1.124863
Particle 1; Weight = -0.941903
", + "Token: `ip`
Context: SELECT vote, z
Step 5; Avg weight = -1.283818
Particle 1; Weight = -6.012813
", + "Token: `code`
Context: SELECT vote, zip
Step 6; Avg weight = -1.382515
Particle 1; Weight = -6.013357
", + "Token: `,`
Context: SELECT vote, zipcode
Step 7; Avg weight = -1.593709
Particle 1; Weight = -6.019923
", + "Token: ` age`
Context: SELECT vote, zipcode,
Step 8; Avg weight = -1.701664
Particle 1; Weight = -6.141646
", + "Token: ` FROM`
Context: SELECT vote, zipcode, age
Step 9; Avg weight = -1.831096
Particle 1; Weight = -6.179917
", + "Token: ` data`
Context: SELECT vote, zipcode, age FROM
Step 10; Avg weight = -1.957496
Particle 1; Weight = -6.337855
", + "Token: ` ORDER`
Context: SELECT vote, zipcode, age FROM data
Step 11; Avg weight = -2.368902
Particle 1; Weight = -6.415601
", + "Token: ` ASC`
Context: SELECT age, zipcode, vote FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 1; Weight = -3.476535
", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 1; Weight = -4.767722
", + "Token: `s`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC Step 15; Avg weight = -5.379061
Particle 1; Weight = -4.768406
", + "Token: `>`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 1; Weight = -4.771100
↳ resampled as particle 1", + "Token: `>`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 2; Weight = -4.654765
↳ resampled as particle 1", + "Token: `>`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 3; Weight = -4.654765
↳ resampled as particle 1", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age DESC
Step 16; Avg weight = -5.428199
Particle 4; Weight = -15.678409
↳ resampled as particle 1", + "Token: `>`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 5; Weight = -4.654765
↳ resampled as particle 1", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 5; Weight = -10.709838
↳ resampled as particle 1", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 6; Weight = -10.709838
↳ resampled as particle 1", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 7; Weight = -10.709838
↳ resampled as particle 1", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 8; Weight = -10.709838
↳ resampled as particle 1", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 9; Weight = -10.709838
↳ resampled as particle 1", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 1; Weight = -8.748671
" ], "marker": { "color": "rgb(231.8181818181818, 11.363636363636363, 23.181818181818183)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, + 14.142135623730951, + 14.142135623730951, 14.426875606340475, - 14.957980686285072, - 15.892654257087203, - 16.31402236648133, - 17.245500514545256, - 17.206990352134213, - 17.728185797044485, - 18.46110809174003, - 21.85761107303393, - 26.208112290860193, - 1.1345929377016004, - 36.391195695925425, - 6.262131678898471, - 1.9293488664715568, - 20.734725349948505, - 15.436206063659489, - 11.463238186987834, - 12.31540406781351, - 0.15130373319520335, - 0.19465423909351, - 16.06780063117456, - 10.754029633087676, - 14.014186391235471, - 15.46026914441878, - 15.618213334861194, - 17.059019228418585, - 17.994791391240355, - 2.49072195705862, - 14.496904283336116, - 14.699874136781524, - 14.756717942311424, - 15.573439985779483, - 15.28583544652617, - 15.322712883876736, - 14.670360050866678, - 14.783954554131531, - 15.257427635017716, - 16.2949765064085, - 16.226621616997722, - 16.44776235188374, - 17.872151427934206, - 18.060575084182805, - 19.351918663300182, - 0.000372799533591042, - 20.12616444032322, - 17.72489907245881, - 0.029807816794460037, - 14.14350971627556, - 14.188055374386854, - 14.23291921712661, - 14.773612751175577, - 14.86708107558635, - 14.886980443348532, - 15.730247329765737, - 15.775373352012783, - 15.85236773559772, - 15.869678558039965, - 15.910575451048258, - 16.128462319886363, - 16.13859850646276, - 16.13440761047257, - 16.12223546334159, - 16.1377660361286, - 16.13005374730457, - 16.152618867083763 + 14.402897315470543, + 15.496880434252066, + 1.3293115047059585, + 1.3961769389377505, + 1.5465886080783529, + 1.5359784163515617, + 1.607609398891269, + 1.5824576420399536, + 1.869757708499547, + 12.525581649649281, + 8.849523267032996, + 19.19186439427444, + 19.642749756869918, + 20.819202376361407, + 20.819202376361407, + 0.08408338265222687, + 20.819202376361407, + 5.304604544335504, + 5.304604544335504, + 5.304604544335504, + 5.304604544335504, + 5.304604544335504, + 15.491603946203218 ] }, "mode": "markers+text", @@ -909,69 +839,28 @@ " SELECT", " vote", ",", - " zip", + " z", + "ip", "code", ",", " age", " FROM", " data", " ORDER", - " BY", - "/", - " age", + " ASC", " ", + ">", + ">", + " ", + " ", + " ", + " ", + " ", + " ", + "▪" ], "type": "scatter", "x": [ @@ -987,61 +876,20 @@ 9, 10, 11, - 12, - 12, - 12, - 12, - 12, - 12, - 12, 13, 14, 15, + 16, + 16, + 16, + 16, + 16, 18, - 19, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 42, - 42, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 18, + 18, + 18, + 18, + 19 ], "y": [ 1, @@ -1056,60 +904,19 @@ 1, 1, 1, - 0, - 1, - 2, - 3, - 4, - 5, - 6, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 0, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, 1, 1, 1, 1, - 1, - 0, - 1, 2, 3, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, - 1, + 4, + 5, + 5, + 6, + 7, + 8, + 9, 1 ] }, @@ -1118,131 +925,43 @@ "hovertext": [ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 2; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 2; Weight = -0.215239
", - "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 2; Weight = -0.734827
", - "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.963722
Particle 2; Weight = -0.760898
", - "Token: ` gender`
Context: SELECT age,
Step 4; Avg weight = -1.175300
Particle 2; Weight = -0.996677
", - "Token: ` FROM`
Context: SELECT age, gender
Step 5; Avg weight = -1.228179
Particle 2; Weight = -1.037804
", - "Token: ` data`
Context: SELECT age, gender FROM
Step 6; Avg weight = -1.345798
Particle 2; Weight = -1.199354
", - "Token: ` ORDER`
Context: SELECT age, gender FROM data
Step 7; Avg weight = -1.463050
Particle 2; Weight = -1.309615
", - "Token: ` BY`
Context: SELECT age, gender FROM data ORDER
Step 8; Avg weight = -1.561001
Particle 2; Weight = -1.310432
", - "Token: ` age`
Context: SELECT age, gender FROM data ORDER BY
Step 9; Avg weight = -1.799961
Particle 2; Weight = -1.377011
", - "Token: ` DES`
Context: SELECT age, gender FROM data ORDER BY age
Step 10; Avg weight = -2.215472
Particle 2; Weight = -1.994577
", - "Token: `C`
Context: SELECT age, gender FROM data ORDER BY age DES
Step 11; Avg weight = -2.579298
Particle 2; Weight = -1.998180
", - "Token: ` DES`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 2; Weight = -3.952125
", - "Token: `C`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DES
Step 14; Avg weight = -4.248732
Particle 2; Weight = -3.955157
", - "Token: ` <`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DESC
Step 15; Avg weight = -4.755635
Particle 2; Weight = -6.821659
", - "Token: `96`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566
Step 18; Avg weight = -6.735819
Particle 2; Weight = -6.480502
", - "Token: `16`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456696
Step 19; Avg weight = -7.171824
Particle 2; Weight = -6.650362
", - "Token: `>`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age DESC Step 20; Avg weight = -7.474923
Particle 0; Weight = -14.188846
↳ resampled as particle 2", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456600 <
Step 20; Avg weight = -7.474923
Particle 1; Weight = -15.608931
↳ resampled as particle 2", - "Token: `199`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730
Step 21; Avg weight = -7.750867
Particle 2; Weight = -7.526174
", - "Token: `66`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199
Step 22; Avg weight = -8.015190
Particle 2; Weight = -7.588521
", - "Token: `98`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966
Step 23; Avg weight = -8.109812
Particle 2; Weight = -7.637308
", - "Token: `364`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698
Step 24; Avg weight = -8.338520
Particle 2; Weight = -7.689134
", - "Token: `4`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364
Step 25; Avg weight = -8.503752
Particle 2; Weight = -7.722246
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456696161992199993231038
Step 26; Avg weight = -8.627681
Particle 1; Weight = -8.052325
↳ resampled as particle 2", - "Token: `698`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644
Step 26; Avg weight = -8.627681
Particle 2; Weight = -8.046665
↳ resampled as particle 2", - "Token: `77`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698
Step 27; Avg weight = -8.744810
Particle 2; Weight = -8.695257
", - "Token: `29`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877
Step 28; Avg weight = -8.810626
Particle 2; Weight = -8.738367
", - "Token: `21`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729
Step 29; Avg weight = -8.849632
Particle 2; Weight = -8.771192
", - "Token: `20`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921
Step 30; Avg weight = -8.994838
Particle 2; Weight = -9.070807
", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120
Step 31; Avg weight = -9.029883
Particle 2; Weight = -9.135465
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064
Step 32; Avg weight = -9.058807
Particle 2; Weight = -9.164965
", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412
Step 33; Avg weight = -9.120276
Particle 2; Weight = -9.189829
", - "Token: `75`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289
Step 34; Avg weight = -9.147265
Particle 2; Weight = -9.219777
", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975
Step 35; Avg weight = -9.236098
Particle 2; Weight = -9.247621
", - "Token: `138`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589
Step 36; Avg weight = -9.379450
Particle 2; Weight = -9.277209
", - "Token: `40`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138
Step 37; Avg weight = -9.395129
Particle 2; Weight = -9.298272
", - "Token: `603`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840
Step 38; Avg weight = -9.434781
Particle 2; Weight = -9.311025
", - "Token: `174`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603
Step 39; Avg weight = -9.618847
Particle 2; Weight = -9.397464
", - "Token: `38`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174
Step 40; Avg weight = -9.650162
Particle 2; Weight = -9.408109
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438
Step 41; Avg weight = -9.792840
Particle 2; Weight = -9.416552
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912274730724
Step 42; Avg weight = -9.884456
Particle 4; Weight = -8.908374
↳ resampled as particle 2", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470
Step 42; Avg weight = -9.884456
Particle 5; Weight = -9.191867
↳ resampled as particle 2", - "Token: `28`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313
Step 43; Avg weight = -9.894529
Particle 2; Weight = -9.894334
", - "Token: `512`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328
Step 44; Avg weight = -9.910677
Particle 2; Weight = -9.900268
", - "Token: `36`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328512
Step 45; Avg weight = -9.921427
Particle 2; Weight = -9.906309
", - "Token: `25`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843132851236
Step 46; Avg weight = -9.999674
Particle 2; Weight = -9.911565
", - "Token: `806`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625
Step 47; Avg weight = -10.018553
Particle 2; Weight = -9.920988
", - "Token: `207`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806
Step 48; Avg weight = -10.029880
Particle 2; Weight = -9.931395
", - "Token: `60`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806207
Step 49; Avg weight = -10.149053
Particle 2; Weight = -9.937253
", - "Token: `31`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328512362580620760
Step 50; Avg weight = -10.160593
Particle 2; Weight = -9.942791
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843132851236258062076031
Step 51; Avg weight = -10.174623
Particle 2; Weight = -10.022834
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806207603112
Step 52; Avg weight = -10.181790
Particle 2; Weight = -10.026830
", - "Token: `79`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328512362580620760311299
Step 53; Avg weight = -10.191848
Particle 2; Weight = -10.031530
", - "Token: `43`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843132851236258062076031129979
Step 54; Avg weight = -10.225102
Particle 2; Weight = -10.036538
", - "Token: `01`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806207603112997943
Step 55; Avg weight = -10.231485
Particle 2; Weight = -10.047528
", - "Token: `33`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328512362580620760311299794301
Step 56; Avg weight = -10.237751
Particle 2; Weight = -10.054629
", - "Token: `29`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843132851236258062076031129979430133
Step 57; Avg weight = -10.244302
Particle 2; Weight = -10.060900
", - "Token: `09`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806207603112997943013329
Step 58; Avg weight = -10.249955
Particle 2; Weight = -10.064609
", - "Token: `6`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431328512362580620760311299794301332909
Step 59; Avg weight = -10.255388
Particle 2; Weight = -10.072078
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313285123625806207603112997943013329096
Step 60; Avg weight = -10.262210
Particle 2; Weight = -10.089051
" + "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 2; Weight = -0.796165
", + "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.888091
Particle 2; Weight = -0.851550
", + "Token: ` age`
Context: SELECT vote,
Step 4; Avg weight = -1.124863
Particle 2; Weight = -0.983349
", + "Token: ` FROM`
Context: SELECT vote, age
Step 5; Avg weight = -1.283818
Particle 2; Weight = -1.085545
", + "Token: ` data`
Context: SELECT vote, age FROM
Step 6; Avg weight = -1.382515
Particle 2; Weight = -1.372316
", + "Token: ` ORDER`
Context: SELECT vote, age FROM data
Step 7; Avg weight = -1.593709
Particle 2; Weight = -1.535216
", + "Token: ` BY`
Context: SELECT vote, age FROM data ORDER
Step 8; Avg weight = -1.701664
Particle 2; Weight = -1.536064
", + "Token: ` age`
Context: SELECT vote, age FROM data ORDER BY
Step 9; Avg weight = -1.831096
Particle 2; Weight = -1.648144
", + "Token: ` ASC`
Context: SELECT vote, age FROM data ORDER BY age
Step 10; Avg weight = -1.957496
Particle 2; Weight = -2.312404
", + "Token: ` <`
Context: SELECT vote, age FROM data ORDER BY age ASC
Step 11; Avg weight = -2.368902
Particle 2; Weight = -3.614424
", + "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 2; Weight = -3.427171
", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 2; Weight = -4.651501
", + "Token: `s`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 15; Avg weight = -5.379061
Particle 2; Weight = -4.652134
", + "Token: `▪`
Context: SELECT vote, zipcode FROM data WHERE age >50
Step 16; Avg weight = -5.428199
Particle 6; Weight = -14.401612
↳ resampled as particle 2", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 2; Weight = -8.748671
" ], "marker": { "color": "rgb(208.63636363636363, 22.727272727272727, 46.36363636363637)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, - 14.876188905320037, - 15.651562248364147, - 15.463306612906164, - 15.554445252926628, - 15.21650837141921, - 15.269787588733452, - 16.029701984972636, - 17.472595535104748, - 15.793621589058835, - 18.91050794932581, - 12.31540406781351, - 16.37812325089876, - 5.033657047718582, - 16.06780063117456, - 18.354769232538416, - 0.492725504890831, - 0.24223532802100892, - 15.82363738134572, - 17.505117525966565, - 17.910916863027445, - 19.56713737923748, - 20.90340058830188, - 18.8561042983736, - 18.909549316311782, - 14.496904283336116, - 14.662425794780567, - 14.707811109281794, - 13.615031633516349, - 13.414922406337045, - 13.411060716815149, - 13.658773292319175, - 13.6385837335622, - 14.060889192363431, - 14.883879886816272, - 14.843874936630792, - 15.04486522804419, - 15.797478388655952, - 15.96158436841074, - 17.06965338311171, - 23.039259302416447, - 19.994414362051252, - 14.14350971627556, - 14.2159313908212, - 14.249437409374554, - 14.779088574438648, - 14.849134251289032, - 14.855966491094623, - 15.721963016787717, - 15.769212840034712, - 15.257229150848827, - 15.281433214174372, - 15.32242581405693, - 15.540364141030992, - 15.504610620375129, - 15.498131513482736, - 15.500303555316783, - 15.515385323028893, - 15.499587446026544, - 15.421119358770635 + 14.142135623730951, + 14.142135623730951, + 14.426875606340475, + 14.402897315470543, + 15.179047012692306, + 15.615986976913133, + 14.214434316632705, + 14.561852234723485, + 15.36294495270749, + 15.49682129995808, + 11.842621115312857, + 7.586706105803062, + 12.838588629338087, + 9.379005638417615, + 20.34066930114752, + 0.15920739647293486, + 15.491603946203218 ] }, "mode": "markers+text", @@ -1250,65 +969,21 @@ "text": [ [], " SELECT", - " age", + " vote", ",", - " gender", + " age", " FROM", " data", " ORDER", " BY", " age", - " DES", - "C", - " DES", - "C", + " ASC", " <", - "96", - "16", - ">", - "/", - "199", - "66", - "98", - "364", - "4", - "12", - "698", - "77", - "29", - "21", - "20", - "64", - "12", - "89", - "75", - "89", - "138", - "40", - "603", - "174", - "38", - "10", - "35", - "64", - "28", - "512", - "36", - "25", - "806", - "207", - "60", - "31", - "12", - "99", - "79", - "43", - "01", - "33", - "29", - "09", - "6", - "13" + " ASC", + " Context:
Step 0; Avg weight = 0.000000
Particle 3; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 3; Weight = -0.215239
", - "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 3; Weight = -0.734827
", - "Token: ` FROM`
Context: SELECT age
Step 3; Avg weight = -0.963722
Particle 3; Weight = -0.952327
", - "Token: ` data`
Context: SELECT age FROM
Step 4; Avg weight = -1.175300
Particle 3; Weight = -1.325075
", - "Token: ` ORDER`
Context: SELECT age FROM data
Step 5; Avg weight = -1.228179
Particle 3; Weight = -1.470867
", - "Token: ` BY`
Context: SELECT age FROM data ORDER
Step 6; Avg weight = -1.345798
Particle 3; Weight = -1.471849
", - "Token: ` age`
Context: SELECT age FROM data ORDER BY
Step 7; Avg weight = -1.463050
Particle 3; Weight = -1.628895
", - "Token: ` ASC`
Context: SELECT age FROM data ORDER BY age
Step 8; Avg weight = -1.561001
Particle 3; Weight = -2.385261
", - "Token: ` `
Context: SELECT age FROM data ORDER BY age ASC
Step 9; Avg weight = -1.799961
Particle 3; Weight = -3.494861
", - "Token: `s`
Context: SELECT age FROM data ORDER BY age ASC Step 10; Avg weight = -2.215472
Particle 3; Weight = -3.495664
", - "Token: `>`
Context: SELECT age FROM data ORDER BY age ASC Step 11; Avg weight = -2.579298
Particle 3; Weight = -3.498493
", - "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 3; Weight = -3.942277
", - "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 14; Avg weight = -4.248732
Particle 3; Weight = -5.166608
", - "Token: `s`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 15; Avg weight = -4.755635
Particle 3; Weight = -5.167241
", - "Token: ` <`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DESC
Step 16; Avg weight = -5.060239
Particle 1; Weight = -16.193516
↳ resampled as particle 3", - "Token: `/`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DESC <
Step 16; Avg weight = -5.060239
Particle 2; Weight = -14.417618
↳ resampled as particle 3", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566
Step 18; Avg weight = -6.735819
Particle 3; Weight = -6.480502
", - "Token: `237`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630
Step 19; Avg weight = -7.171824
Particle 3; Weight = -6.602327
", - "Token: `1992`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669616
Step 20; Avg weight = -7.474923
Particle 2; Weight = -6.763732
↳ resampled as particle 3", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566999967
Step 21; Avg weight = -7.750867
Particle 3; Weight = -7.559941
", - "Token: `32`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456699996745
Step 22; Avg weight = -8.015190
Particle 3; Weight = -7.635053
", - "Token: `00`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669999674532
Step 23; Avg weight = -8.109812
Particle 3; Weight = -7.777324
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566999967453200
Step 24; Avg weight = -8.338520
Particle 3; Weight = -9.267449
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566999967453200 <
Step 25; Avg weight = -8.503752
Particle 3; Weight = -16.677927
", - "Token: `80`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677
Step 27; Avg weight = -8.744810
Particle 3; Weight = -8.665921
", - "Token: `216`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780
Step 28; Avg weight = -8.810626
Particle 3; Weight = -8.697825
", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216
Step 29; Avg weight = -8.849632
Particle 3; Weight = -8.725612
", - "Token: `440`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566778021664
Step 30; Avg weight = -8.994838
Particle 3; Weight = -8.753382
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566778021664440
Step 31; Avg weight = -9.029883
Particle 3; Weight = -8.768066
", - "Token: `350`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677802166444099
Step 32; Avg weight = -9.058807
Particle 3; Weight = -8.787489
", - "Token: `73`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677802166444099350
Step 33; Avg weight = -9.120276
Particle 3; Weight = -8.803291
", - "Token: `268`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073
Step 34; Avg weight = -9.147265
Particle 3; Weight = -8.832398
", - "Token: `451`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268
Step 35; Avg weight = -9.236098
Particle 3; Weight = -8.854444
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451
Step 36; Avg weight = -9.379450
Particle 3; Weight = -9.941605
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451 Step 37; Avg weight = -9.395129
Particle 3; Weight = -9.944162
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451 Step 38; Avg weight = -9.434781
Particle 3; Weight = -9.955730
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451
Step 39; Avg weight = -9.618847
Particle 3; Weight = -14.663533
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451
Step 40; Avg weight = -9.650162
Particle 3; Weight = -22.208742
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667780216644409935073268451
Step 41; Avg weight = -9.792840
Particle 3; Weight = -22.208742
", - "Token: `18`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469823737810821755318899617631301376184313
Step 43; Avg weight = -9.894529
Particle 3; Weight = -9.894334
", - "Token: `93`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446982373781082175531889961763130137618431318
Step 44; Avg weight = -9.910677
Particle 3; Weight = -9.899103
", - "Token: `509`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893
Step 45; Avg weight = -9.921427
Particle 3; Weight = -9.911078
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 46; Avg weight = -9.999674
Particle 3; Weight = -11.401761
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509 Step 47; Avg weight = -10.018553
Particle 3; Weight = -11.406611
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509 Step 48; Avg weight = -10.029880
Particle 3; Weight = -11.423244
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 49; Avg weight = -10.149053
Particle 3; Weight = -15.816724
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 50; Avg weight = -10.160593
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 51; Avg weight = -10.174623
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 52; Avg weight = -10.181790
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 53; Avg weight = -10.191848
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 54; Avg weight = -10.225102
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 55; Avg weight = -10.231485
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 56; Avg weight = -10.237751
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 57; Avg weight = -10.244302
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 58; Avg weight = -10.249955
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 59; Avg weight = -10.255388
Particle 3; Weight = -23.188587
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698237378108217553188996176313013761843131893509
Step 60; Avg weight = -10.262210
Particle 3; Weight = -23.188587
" + "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 3; Weight = -0.796165
", + "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.888091
Particle 3; Weight = -0.851550
", + "Token: ` zip`
Context: SELECT vote,
Step 4; Avg weight = -1.124863
Particle 3; Weight = -0.941903
", + "Token: `code`
Context: SELECT vote, zip
Step 5; Avg weight = -1.283818
Particle 3; Weight = -0.942447
", + "Token: `,`
Context: SELECT vote, zipcode
Step 6; Avg weight = -1.382515
Particle 3; Weight = -0.949013
", + "Token: ` age`
Context: SELECT vote, zipcode,
Step 7; Avg weight = -1.593709
Particle 3; Weight = -1.070736
", + "Token: ` FROM`
Context: SELECT vote, zipcode, age
Step 8; Avg weight = -1.701664
Particle 3; Weight = -1.109007
", + "Token: ` data`
Context: SELECT vote, zipcode, age FROM
Step 9; Avg weight = -1.831096
Particle 3; Weight = -1.266945
", + "Token: ` ORDER`
Context: SELECT vote, zipcode, age FROM data
Step 10; Avg weight = -1.957496
Particle 3; Weight = -1.344691
", + "Token: ` BY`
Context: SELECT vote, zipcode, age FROM data ORDER
Step 11; Avg weight = -2.368902
Particle 3; Weight = -1.345477
", + "Token: `/`
Context: SELECT vote, age FROM data ORDER BY age ASC <
Step 12; Avg weight = -2.817760
Particle 2; Weight = -10.050104
↳ resampled as particle 3", + "Token: ` age`
Context: SELECT vote, zipcode, age FROM data ORDER BY
Step 12; Avg weight = -2.817760
Particle 3; Weight = -1.442531
↳ resampled as particle 3", + "Token: ` `
Context: SELECT vote, zipcode FROM data WHERE age >50
Step 12; Avg weight = -2.817760
Particle 4; Weight = -2.945692
↳ resampled as particle 3", + "Token: ` BY`
Context: SELECT age, vote FROM data WHERE age > 30 ORDER
Step 12; Avg weight = -2.817760
Particle 5; Weight = -4.239493
↳ resampled as particle 3", + "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 3; Weight = -3.427171
", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 3; Weight = -4.651501
", + "Token: `s`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 15; Avg weight = -5.379061
Particle 3; Weight = -4.652134
", + "Token: `▪`
Context: SELECT vote, zipcode FROM data WHERE age >50
Step 16; Avg weight = -5.428199
Particle 7; Weight = -14.401612
↳ resampled as particle 3", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY age ASC
Step 16; Avg weight = -5.428199
Particle 8; Weight = -14.851372
↳ resampled as particle 3", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 2; Weight = -9.179713
↳ resampled as particle 3", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 10; Weight = -10.709838
↳ resampled as particle 3", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 3; Weight = -8.748671
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Step 0; Avg weight = 0.000000
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Context:
Step 1; Avg weight = -0.215239
Particle 4; Weight = -0.215239
", "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 4; Weight = -0.796165
", - "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.963722
Particle 4; Weight = -0.851550
", - "Token: ` age`
Context: SELECT vote,
Step 4; Avg weight = -1.175300
Particle 4; Weight = -0.983349
", - "Token: `,`
Context: SELECT vote, age
Step 5; Avg weight = -1.228179
Particle 4; Weight = -0.996135
", - "Token: ` zip`
Context: SELECT vote, age,
Step 6; Avg weight = -1.345798
Particle 4; Weight = -1.158935
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Context: SELECT vote, age, zip
Step 7; Avg weight = -1.463050
Particle 4; Weight = -1.159698
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Context: SELECT vote, age, zipcode
Step 8; Avg weight = -1.561001
Particle 4; Weight = -1.167358
", - "Token: ` vote`
Context: SELECT vote, age, zipcode,
Step 9; Avg weight = -1.799961
Particle 4; Weight = -1.743222
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Context: SELECT vote, age, zipcode, vote
Step 10; Avg weight = -2.215472
Particle 4; Weight = -2.241393
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Context: SELECT vote, age, zipcode, vote FROM
Step 11; Avg weight = -2.579298
Particle 4; Weight = -2.441249
", - "Token: `▪`
Context: SELECT vote FROM data ORDER BY age ASC
Step 12; Avg weight = -3.332867
Particle 7; Weight = -6.689906
↳ resampled as particle 4", - "Token: ` <`
Context: SELECT vote, age FROM data ORDER BY age DESC
Step 12; Avg weight = -3.332867
Particle 8; Weight = -5.400610
↳ resampled as particle 4", - "Token: `>`
Context: SELECT age FROM data WHERE age>50 Step 12; Avg weight = -3.332867
Particle 9; Weight = -8.632386
↳ resampled as particle 4", - "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 4; Weight = -3.942277
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Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 14; Avg weight = -4.248732
Particle 4; Weight = -5.166608
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Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 15; Avg weight = -4.755635
Particle 4; Weight = -5.167241
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Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 16; Avg weight = -5.060239
Particle 3; Weight = -5.169872
↳ resampled as particle 4", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566
Step 18; Avg weight = -6.735819
Particle 4; Weight = -6.480502
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456699
Step 19; Avg weight = -7.171824
Particle 4; Weight = -6.611262
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237
Step 20; Avg weight = -7.474923
Particle 3; Weight = -6.653268
↳ resampled as particle 4", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001 <
Step 21; Avg weight = -7.750867
Particle 4; Weight = -15.431508
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Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001 Step 22; Avg weight = -8.015190
Particle 4; Weight = -15.436604
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001 Step 23; Avg weight = -8.109812
Particle 4; Weight = -15.450167
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001
Step 24; Avg weight = -8.338520
Particle 4; Weight = -20.948413
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001
Step 25; Avg weight = -8.503752
Particle 4; Weight = -28.740011
", - "Token: `46`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677
Step 27; Avg weight = -8.744810
Particle 4; Weight = -8.665921
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746
Step 28; Avg weight = -8.810626
Particle 4; Weight = -8.703921
", - "Token: `345`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630
Step 29; Avg weight = -8.849632
Particle 4; Weight = -8.725066
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Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345
Step 30; Avg weight = -8.994838
Particle 4; Weight = -8.750778
", - "Token: `46`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530
Step 31; Avg weight = -9.029883
Particle 4; Weight = -8.767965
", - "Token: `22`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046
Step 32; Avg weight = -9.058807
Particle 4; Weight = -8.786316
", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622
Step 33; Avg weight = -9.120276
Particle 4; Weight = -8.803689
", - "Token: `697`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245
Step 34; Avg weight = -9.147265
Particle 4; Weight = -8.812720
", - "Token: `888`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697
Step 35; Avg weight = -9.236098
Particle 4; Weight = -8.824115
", - "Token: `139`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888
Step 36; Avg weight = -9.379450
Particle 4; Weight = -8.840167
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139
Step 37; Avg weight = -9.395129
Particle 4; Weight = -8.849677
", - "Token: `27`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912
Step 38; Avg weight = -9.434781
Particle 4; Weight = -8.862489
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227
Step 39; Avg weight = -9.618847
Particle 4; Weight = -8.872884
", - "Token: `307`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747
Step 40; Avg weight = -9.650162
Particle 4; Weight = -8.880528
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307
Step 41; Avg weight = -9.792840
Particle 4; Weight = -8.895231
", - "Token: `349`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165
Step 42; Avg weight = -9.884456
Particle 6; Weight = -9.319121
↳ resampled as particle 4", - "Token: `63`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659
Step 43; Avg weight = -9.894529
Particle 4; Weight = -9.893917
", - "Token: `23`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963
Step 44; Avg weight = -9.910677
Particle 4; Weight = -9.987865
", - "Token: `405`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106596323
Step 45; Avg weight = -9.921427
Particle 4; Weight = -9.999190
", - "Token: `300`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106596323405
Step 46; Avg weight = -9.999674
Particle 4; Weight = -10.010523
", - "Token: `27`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106596323405300
Step 47; Avg weight = -10.018553
Particle 4; Weight = -10.030272
", - "Token: `05`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659632340530027
Step 48; Avg weight = -10.029880
Particle 4; Weight = -10.039741
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705
Step 49; Avg weight = -10.149053
Particle 4; Weight = -14.256327
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705
Step 50; Avg weight = -10.160593
Particle 4; Weight = -16.140834
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705
Step 51; Avg weight = -10.174623
Particle 4; Weight = -16.169179
", - "Token: `O`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705
Step 52; Avg weight = -10.181790
Particle 4; Weight = -24.844946
", - "Token: `R`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 O
Step 53; Avg weight = -10.191848
Particle 4; Weight = -33.247582
", - "Token: `DE`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 OR
Step 54; Avg weight = -10.225102
Particle 4; Weight = -45.285317
", - "Token: `R`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDE
Step 55; Avg weight = -10.231485
Particle 4; Weight = -53.027409
", - "Token: ` BY`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDER
Step 56; Avg weight = -10.237751
Particle 4; Weight = -53.029973
", - "Token: ` age`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDER BY
Step 57; Avg weight = -10.244302
Particle 4; Weight = -53.312084
", - "Token: ` ASC`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDER BY age
Step 58; Avg weight = -10.249955
Particle 4; Weight = -54.015871
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDER BY age ASC
Step 59; Avg weight = -10.255388
Particle 4; Weight = -54.606759
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065963234053002705 ORDER BY age ASC Step 60; Avg weight = -10.262210
Particle 4; Weight = -54.608153
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Context: SELECT vote
Step 3; Avg weight = -0.888091
Particle 4; Weight = -0.851550
", + "Token: ` zip`
Context: SELECT vote,
Step 4; Avg weight = -1.124863
Particle 4; Weight = -0.941903
", + "Token: `code`
Context: SELECT vote, zip
Step 5; Avg weight = -1.283818
Particle 4; Weight = -0.942447
", + "Token: ` FROM`
Context: SELECT vote, zipcode
Step 6; Avg weight = -1.382515
Particle 4; Weight = -1.031305
", + "Token: ` data`
Context: SELECT vote, zipcode FROM
Step 7; Avg weight = -1.593709
Particle 4; Weight = -1.193927
", + "Token: ` WHERE`
Context: SELECT vote, zipcode FROM data
Step 8; Avg weight = -1.701664
Particle 4; Weight = -1.318863
", + "Token: ` age`
Context: SELECT vote, zipcode FROM data WHERE
Step 9; Avg weight = -1.831096
Particle 4; Weight = -1.519938
", + "Token: ` >`
Context: SELECT vote, zipcode FROM data WHERE age
Step 10; Avg weight = -1.957496
Particle 4; Weight = -1.634965
", + "Token: `50`
Context: SELECT vote, zipcode FROM data WHERE age >
Step 11; Avg weight = -2.368902
Particle 4; Weight = -1.652492
", + "Token: `ip`
Context: SELECT age, vote, zipcode, vote, z
Step 12; Avg weight = -2.817760
Particle 6; Weight = -6.819480
↳ resampled as particle 4", + "Token: `ity`
Context: SELECT state_color FROM data WHERE race_ethnic
Step 12; Avg weight = -2.817760
Particle 7; Weight = -14.102093
↳ resampled as particle 4", + "Token: ` DE`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 4; Weight = -3.437018
", + "Token: `S`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DE
Step 14; Avg weight = -3.830131
Particle 4; Weight = -12.808875
", + "Token: `C`
Context: SELECT vote, zipcode, age FROM data ORDER BY age DES
Step 15; Avg weight = -5.379061
Particle 4; Weight = -12.811907
", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 4; Weight = -8.748671
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Context:
Step 0; Avg weight = 0.000000
Particle 5; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 5; Weight = -0.215239
", "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 5; Weight = -0.734827
", - "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.963722
Particle 5; Weight = -0.760898
", - "Token: ` gender`
Context: SELECT age,
Step 4; Avg weight = -1.175300
Particle 5; Weight = -0.996677
", - "Token: ` FROM`
Context: SELECT age, gender
Step 5; Avg weight = -1.228179
Particle 5; Weight = -1.037804
", - "Token: ` data`
Context: SELECT age, gender FROM
Step 6; Avg weight = -1.345798
Particle 5; Weight = -1.199354
", - "Token: ` ORDER`
Context: SELECT age, gender FROM data
Step 7; Avg weight = -1.463050
Particle 5; Weight = -1.309615
", - "Token: ` BY`
Context: SELECT age, gender FROM data ORDER
Step 8; Avg weight = -1.561001
Particle 5; Weight = -1.310432
", - "Token: ` age`
Context: SELECT age, gender FROM data ORDER BY
Step 9; Avg weight = -1.799961
Particle 5; Weight = -1.377011
", - "Token: ` ASC`
Context: SELECT age, gender FROM data ORDER BY age
Step 10; Avg weight = -2.215472
Particle 5; Weight = -2.002698
", - "Token: ` `
Context: SELECT age, gender FROM data ORDER BY age ASC
Step 11; Avg weight = -2.579298
Particle 5; Weight = -3.157225
", - "Token: `,`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 5; Weight = -3.435436
", - "Token: ` vote`
Context: SELECT vote, zipcode, age FROM data ORDER BY age,
Step 14; Avg weight = -4.248732
Particle 5; Weight = -5.081013
", - "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age, vote
Step 15; Avg weight = -4.755635
Particle 5; Weight = -6.050642
", - "Token: `97`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566
Step 18; Avg weight = -6.735819
Particle 5; Weight = -6.480502
", - "Token: `001`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697
Step 19; Avg weight = -7.171824
Particle 5; Weight = -6.613795
", - "Token: `67`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945669999
Step 20; Avg weight = -7.474923
Particle 4; Weight = -6.641805
↳ resampled as particle 5", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546 Step 21; Avg weight = -7.750867
Particle 5; Weight = -7.492887
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 22; Avg weight = -8.015190
Particle 5; Weight = -13.046424
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 23; Avg weight = -8.109812
Particle 5; Weight = -20.463654
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 24; Avg weight = -8.338520
Particle 5; Weight = -20.463654
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 25; Avg weight = -8.503752
Particle 5; Weight = -20.463654
", - "Token: `401`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677
Step 27; Avg weight = -8.744810
Particle 5; Weight = -8.665921
", - "Token: `313`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401
Step 28; Avg weight = -8.810626
Particle 5; Weight = -8.683687
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313
Step 29; Avg weight = -8.849632
Particle 5; Weight = -8.703401
", - "Token: `16`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347
Step 30; Avg weight = -8.994838
Particle 5; Weight = -8.721304
", - "Token: `01`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716
Step 31; Avg weight = -9.029883
Particle 5; Weight = -8.742480
", - "Token: `77`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601
Step 32; Avg weight = -9.058807
Particle 5; Weight = -8.774258
", - "Token: `36`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177
Step 33; Avg weight = -9.120276
Particle 5; Weight = -8.802013
", - "Token: `49`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736
Step 34; Avg weight = -9.147265
Particle 5; Weight = -8.822348
", - "Token: `06`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649
Step 35; Avg weight = -9.236098
Particle 5; Weight = -8.881627
", - "Token: `407`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906
Step 36; Avg weight = -9.379450
Particle 5; Weight = -8.916935
", - "Token: `703`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407
Step 37; Avg weight = -9.395129
Particle 5; Weight = -8.930628
", - "Token: `157`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703
Step 38; Avg weight = -9.434781
Particle 5; Weight = -9.018844
", - "Token: `62`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157
Step 39; Avg weight = -9.618847
Particle 5; Weight = -9.031326
", - "Token: `234`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762
Step 40; Avg weight = -9.650162
Particle 5; Weight = -9.165037
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234
Step 41; Avg weight = -9.792840
Particle 5; Weight = -9.182985
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 42; Avg weight = -9.884456
Particle 7; Weight = -23.134801
↳ resampled as particle 5", - "Token: `465`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950
Step 42; Avg weight = -9.884456
Particle 8; Weight = -9.219106
↳ resampled as particle 5", - "Token: `244`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659
Step 43; Avg weight = -9.894529
Particle 5; Weight = -9.893917
", - "Token: `18`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659244
Step 44; Avg weight = -9.910677
Particle 5; Weight = -9.901661
", - "Token: `93`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418
Step 45; Avg weight = -9.921427
Particle 5; Weight = -9.909738
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106592441893
Step 46; Avg weight = -9.999674
Particle 5; Weight = -9.925788
", - "Token: `59`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659244189324
Step 47; Avg weight = -10.018553
Particle 5; Weight = -9.934456
", - "Token: `11`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459
Step 48; Avg weight = -10.029880
Particle 5; Weight = -9.942554
", - "Token: `69`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106592441893245911
Step 49; Avg weight = -10.149053
Particle 5; Weight = -9.959445
", - "Token: `18`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659244189324591169
Step 50; Avg weight = -10.160593
Particle 5; Weight = -9.964344
", - "Token: `750`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459116918
Step 51; Avg weight = -10.174623
Particle 5; Weight = -9.969881
", - "Token: `96`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459116918750
Step 52; Avg weight = -10.181790
Particle 5; Weight = -9.973676
", - "Token: `3`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106592441893245911691875096
Step 53; Avg weight = -10.191848
Particle 5; Weight = -9.980961
", - "Token: `196`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459116918750963
Step 54; Avg weight = -10.225102
Particle 5; Weight = -10.026870
", - "Token: `36`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459116918750963196
Step 55; Avg weight = -10.231485
Particle 5; Weight = -10.029600
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106592441893245911691875096319636
Step 56; Avg weight = -10.237751
Particle 5; Weight = -10.033507
", - "Token: `86`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659244189324591169187509631963635
Step 57; Avg weight = -10.244302
Particle 5; Weight = -10.039161
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456630237301996698364469877292120641289758913840603174381065924418932459116918750963196363586
Step 58; Avg weight = -10.249955
Particle 5; Weight = -10.044649
", - "Token: `42`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945663023730199669836446987729212064128975891384060317438106592441893245911691875096319636358630
Step 59; Avg weight = -10.255388
Particle 5; Weight = -10.048216
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566302373019966983644698772921206412897589138406031743810659244189324591169187509631963635863042
Step 60; Avg weight = -10.262210
Particle 5; Weight = -10.059225
" + "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 5; Weight = -0.760898
", + "Token: ` vote`
Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 5; Weight = -1.444720
", + "Token: ` FROM`
Context: SELECT age, vote
Step 5; Avg weight = -1.283818
Particle 5; Weight = -2.001568
", + "Token: ` data`
Context: SELECT age, vote FROM
Step 6; Avg weight = -1.382515
Particle 5; Weight = -2.253409
", + "Token: ` WHERE`
Context: SELECT age, vote FROM data
Step 7; Avg weight = -1.593709
Particle 5; Weight = -2.448986
", + "Token: ` age`
Context: SELECT age, vote FROM data WHERE
Step 8; Avg weight = -1.701664
Particle 5; Weight = -2.645728
", + "Token: ` >`
Context: SELECT age, vote FROM data WHERE age
Step 9; Avg weight = -1.831096
Particle 5; Weight = -2.759602
", + "Token: ` 30`
Context: SELECT age, vote FROM data WHERE age >
Step 10; Avg weight = -1.957496
Particle 5; Weight = -2.814054
", + "Token: ` ORDER`
Context: SELECT age, vote FROM data WHERE age > 30
Step 11; Avg weight = -2.368902
Particle 5; Weight = -4.235426
", + "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 5; Weight = -3.427171
", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 5; Weight = -4.651501
", + "Token: `s`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 15; Avg weight = -5.379061
Particle 5; Weight = -4.652134
", + "Token: `s`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC Step 16; Avg weight = -5.428199
Particle 9; Weight = -4.935700
↳ resampled as particle 5", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 5; Weight = -8.748671
" ], "marker": { "color": "rgb(139.09090909090907, 56.81818181818181, 115.90909090909092)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, + 14.142135623730951, + 14.142135623730951, 14.876188905320037, - 15.651562248364147, - 15.463306612906164, - 15.554445252926628, - 15.21650837141921, - 15.269787588733452, - 16.029701984972636, - 17.472595535104748, - 15.729628515384276, - 10.593020645229378, - 15.945796047746583, - 9.32797927113191, - 7.40129765930505, - 16.06780063117456, - 18.69344730191678, - 21.449847495548923, - 16.089209146104615, - 1.1428689118690558, - 0.029370567612166533, - 0.03292878435368062, - 0.03576477291166867, - 14.711115761904978, - 15.06882692324943, - 15.214885250884318, - 16.21482018700445, - 16.327648823633293, - 16.30436528197366, - 16.58154081114684, - 16.636806169110297, - 16.88446538614149, - 17.82168073439095, - 17.839397371082573, - 17.411439443152904, - 18.971149588264613, - 18.02429882438961, - 19.18417985861736, - 0.018760270656962254, - 19.72395149894489, - 14.14646024941508, - 14.206036220581456, - 14.225025392420347, - 14.674355011931318, - 14.749471199314248, - 14.773304800883375, - 15.54847084189184, - 15.600185788408972, - 15.666578945991604, - 15.693011665283771, - 15.714788598472154, - 15.615665579991523, - 15.644216826988083, - 15.66267671843806, - 15.669703917909477, - 15.671001638747216, - 15.685622953131814, - 15.652818206515422 + 15.07074040392218, + 12.051994367230447, + 9.87774120913102, + 9.149613689951803, + 9.221337530516655, + 8.820921151020022, + 8.889811190710606, + 9.215436539735505, + 5.561659042242515, + 12.838588629338087, + 9.379005638417615, + 20.34066930114752, + 18.090888691679652, + 15.491603946203218 ] }, "mode": "markers+text", @@ -2206,60 +1326,19 @@ " SELECT", " age", ",", - " gender", + " vote", " FROM", " data", - " ORDER", - " BY", + " WHERE", " age", + " >", + " 30", + " ORDER", " ASC", " ", - " ", - "▪", - "▪", - "▪", - "401", - "313", - "47", - "16", - "01", - "77", - "36", - "49", - "06", - "407", - "703", - "157", - "62", - "234", - "70", - "▪", - "465", - "244", - "18", - "93", - "24", - "59", - "11", - "69", - "18", - "750", - "96", - "3", - "196", - "36", - "35", - "86", - "30", - "42", - "13" + "s", + "s", + "▪" ], "type": "scatter", "x": [ @@ -2278,49 +1357,8 @@ 13, 14, 15, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 16, + 19 ], "y": [ 5, @@ -2338,48 +1376,7 @@ 5, 5, 5, - 5, - 5, - 4, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 7, - 8, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, - 5, + 9, 5 ] }, @@ -2389,132 +1386,40 @@ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 6; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 6; Weight = -0.215239
", "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 6; Weight = -0.734827
", - "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.963722
Particle 6; Weight = -0.760898
", - "Token: ` zip`
Context: SELECT age,
Step 4; Avg weight = -1.175300
Particle 6; Weight = -0.976939
", - "Token: `code`
Context: SELECT age, zip
Step 5; Avg weight = -1.228179
Particle 6; Weight = -0.977817
", - "Token: ` FROM`
Context: SELECT age, zipcode
Step 6; Avg weight = -1.345798
Particle 6; Weight = -1.038378
", - "Token: ` data`
Context: SELECT age, zipcode FROM
Step 7; Avg weight = -1.463050
Particle 6; Weight = -1.215785
", - "Token: ` WHERE`
Context: SELECT age, zipcode FROM data
Step 8; Avg weight = -1.561001
Particle 6; Weight = -1.341486
", - "Token: ` vote`
Context: SELECT age, zipcode FROM data WHERE
Step 9; Avg weight = -1.799961
Particle 6; Weight = -1.660543
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote
Step 10; Avg weight = -2.215472
Particle 6; Weight = -3.014423
", - "Token: ` 50`
Context: SELECT age, zipcode FROM data WHERE vote <
Step 11; Avg weight = -2.579298
Particle 6; Weight = -3.327707
", - "Token: ` 100`
Context: SELECT state_color FROM data WHERE state_color >
Step 12; Avg weight = -3.332867
Particle 10; Weight = -6.135816
↳ resampled as particle 6", - "Token: ` `
Context: SELECT vote, zipcode FROM data ORDER BY age ASC
Step 12; Avg weight = -3.332867
Particle 11; Weight = -3.402973
↳ resampled as particle 6", - "Token: `,`
Context: SELECT vote, zipcode, age FROM data ORDER BY age
Step 13; Avg weight = -3.675509
Particle 6; Weight = -3.435436
", - "Token: ` age`
Context: SELECT vote, zipcode, age FROM data ORDER BY age,
Step 14; Avg weight = -4.248732
Particle 6; Weight = -4.844471
", - "Token: ` ASC`
Context: SELECT vote, zipcode, age FROM data ORDER BY age, age
Step 15; Avg weight = -4.755635
Particle 6; Weight = -6.227288
", - "Token: `46`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545
Step 18; Avg weight = -6.735819
Particle 6; Weight = -6.503222
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 19; Avg weight = -7.171824
Particle 6; Weight = -7.706608
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001
Step 20; Avg weight = -7.474923
Particle 5; Weight = -8.166442
↳ resampled as particle 6", - "Token: `60`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966
Step 21; Avg weight = -7.750867
Particle 6; Weight = -7.576239
", - "Token: `429`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660
Step 22; Avg weight = -8.015190
Particle 6; Weight = -7.634329
", - "Token: `2`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429
Step 23; Avg weight = -8.109812
Particle 6; Weight = -7.664357
", - "Token: `245`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292
Step 24; Avg weight = -8.338520
Particle 6; Weight = -8.015766
", - "Token: `66`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245
Step 25; Avg weight = -8.503752
Particle 6; Weight = -8.035995
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494566999967453200 Step 26; Avg weight = -8.627681
Particle 3; Weight = -16.683413
↳ resampled as particle 6", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456697001
Step 26; Avg weight = -8.627681
Particle 4; Weight = -28.740011
↳ resampled as particle 6", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546
Step 26; Avg weight = -8.627681
Particle 5; Weight = -20.463654
↳ resampled as particle 6", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789
Step 27; Avg weight = -8.744810
Particle 6; Weight = -8.672037
", - "Token: `54`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950
Step 28; Avg weight = -8.810626
Particle 6; Weight = -8.703970
", - "Token: `165`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054
Step 29; Avg weight = -8.849632
Particle 6; Weight = -8.745037
", - "Token: `22`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165
Step 30; Avg weight = -8.994838
Particle 6; Weight = -8.781121
", - "Token: `18`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522
Step 31; Avg weight = -9.029883
Particle 6; Weight = -8.818154
", - "Token: `183`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218
Step 32; Avg weight = -9.058807
Particle 6; Weight = -8.843619
", - "Token: `759`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183
Step 33; Avg weight = -9.120276
Particle 6; Weight = -8.895759
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759
Step 34; Avg weight = -9.147265
Particle 6; Weight = -8.945931
", - "Token: `40`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935
Step 35; Avg weight = -9.236098
Particle 6; Weight = -8.962050
", - "Token: `37`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540
Step 36; Avg weight = -9.379450
Particle 6; Weight = -8.971216
", - "Token: `72`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037
Step 37; Avg weight = -9.395129
Particle 6; Weight = -8.988162
", - "Token: `60`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772
Step 38; Avg weight = -9.434781
Particle 6; Weight = -9.027434
", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260
Step 39; Avg weight = -9.618847
Particle 6; Weight = -9.047619
", - "Token: `2`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089
Step 40; Avg weight = -9.650162
Particle 6; Weight = -9.066015
", - "Token: `165`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892
Step 41; Avg weight = -9.792840
Particle 6; Weight = -9.294799
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923474015642959950984173588492805 <
Step 42; Avg weight = -9.884456
Particle 9; Weight = -17.495718
↳ resampled as particle 6", - "Token: `307`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435
Step 43; Avg weight = -9.894529
Particle 6; Weight = -9.895948
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307
Step 44; Avg weight = -9.910677
Particle 6; Weight = -9.903930
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724
Step 45; Avg weight = -9.921427
Particle 6; Weight = -9.910158
", - "Token: `307`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912274730724353072435
Step 46; Avg weight = -9.999674
Particle 6; Weight = -9.928198
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912274730724353072435307
Step 47; Avg weight = -10.018553
Particle 6; Weight = -9.936538
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307243530724
Step 48; Avg weight = -10.029880
Particle 6; Weight = -9.943464
", - "Token: `507`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435
Step 49; Avg weight = -10.149053
Particle 6; Weight = -9.977930
", - "Token: `307`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435507
Step 50; Avg weight = -10.160593
Particle 6; Weight = -9.987092
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435507307
Step 51; Avg weight = -10.174623
Particle 6; Weight = -9.990415
", - "Token: `35`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912274730724353072435307243550730724
Step 52; Avg weight = -10.181790
Particle 6; Weight = -9.995305
", - "Token: `705`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307243530724355073072435
Step 53; Avg weight = -10.191848
Particle 6; Weight = -10.019069
", - "Token: `33`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307243530724355073072435705
Step 54; Avg weight = -10.225102
Particle 6; Weight = -10.030166
", - "Token: `37`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435507307243570533
Step 55; Avg weight = -10.231485
Particle 6; Weight = -10.040747
", - "Token: `23`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667746303453046224569788813912274730724353072435307243550730724357053337
Step 56; Avg weight = -10.237751
Particle 6; Weight = -10.048544
", - "Token: `48`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307243530724355073072435705333723
Step 57; Avg weight = -10.244302
Particle 6; Weight = -10.057275
", - "Token: `267`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435507307243570533372348
Step 58; Avg weight = -10.249955
Particle 6; Weight = -10.062262
", - "Token: `1967`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677463034530462245697888139122747307243530724353072435507307243570533372348267
Step 59; Avg weight = -10.255388
Particle 6; Weight = -10.067736
", - "Token: `227`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774630345304622456978881391227473072435307243530724355073072435705333723482671967
Step 60; Avg weight = -10.262210
Particle 6; Weight = -10.072461
" + "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 6; Weight = -0.760898
", + "Token: ` vote`
Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 6; Weight = -1.444720
", + "Token: `,`
Context: SELECT age, vote
Step 5; Avg weight = -1.283818
Particle 6; Weight = -1.520089
", + "Token: ` zip`
Context: SELECT age, vote,
Step 6; Avg weight = -1.382515
Particle 6; Weight = -1.607769
", + "Token: `code`
Context: SELECT age, vote, zip
Step 7; Avg weight = -1.593709
Particle 6; Weight = -1.608235
", + "Token: `,`
Context: SELECT age, vote, zipcode
Step 8; Avg weight = -1.701664
Particle 6; Weight = -1.616995
", + "Token: ` vote`
Context: SELECT age, vote, zipcode,
Step 9; Avg weight = -1.831096
Particle 6; Weight = -2.030693
", + "Token: `,`
Context: SELECT age, vote, zipcode, vote
Step 10; Avg weight = -1.957496
Particle 6; Weight = -2.104259
", + "Token: ` z`
Context: SELECT age, vote, zipcode, vote,
Step 11; Avg weight = -2.368902
Particle 6; Weight = -2.311381
", + "Token: `s`
Context: SELECT vote, zipcode FROM data WHERE age >50 Step 13; Avg weight = -3.233764
Particle 6; Weight = -2.820425
", + "Token: `>`
Context: SELECT vote, zipcode FROM data WHERE age >50 Step 14; Avg weight = -3.830131
Particle 6; Weight = -2.825942
", + "Token: ` `
Context: SELECT vote, zipcode FROM data WHERE age >50
Step 15; Avg weight = -5.379061
Particle 6; Weight = -7.564076
", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 6; Weight = -8.748671
" ], "marker": { "color": "rgb(115.9090909090909, 68.18181818181819, 139.0909090909091)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, + 14.142135623730951, + 14.142135623730951, 14.876188905320037, - 15.651562248364147, - 15.61667483030906, - 16.028041460116725, - 16.491890464498624, - 16.00323540119523, - 15.78272795847303, - 15.1631404638932, - 9.484731508192173, - 9.727475004752804, - 3.4822698244499852, - 13.654996900744372, - 15.945796047746583, - 10.499097017128395, - 6.775630551597999, - 15.88630636524312, - 10.82400731765876, - 10.008143148504011, - 15.432451984784649, - 17.10874029962827, - 17.67031382373654, - 16.618816651520955, - 17.868461333101507, - 0.251903987048936, - 0.0006069850453946611, - 0.03805101718355999, - 14.666197131745148, - 14.91677385878, - 14.901416478076433, - 15.737045333367757, - 15.721402450694944, - 15.74861557942721, - 15.82225038491843, - 15.639909095661604, - 16.21898609845427, - 17.344503337563694, - 17.33352593624652, - 17.336815394872794, - 18.81722989486912, - 18.939169719373695, - 18.14108145508794, - 0.31459390565579026, - 14.13209947459913, - 14.189924635818317, - 14.222038079751762, - 14.656684944164098, - 14.734124904862709, - 14.766586741628332, - 15.405426248298204, - 15.423761348897386, - 15.506556577225648, - 15.524210272856038, - 15.418192762968369, - 15.58995588705359, - 15.557266852956763, - 15.545359997051657, - 15.52842733391254, - 15.533597506509304, - 15.53327773881815, - 15.549571664645482 + 15.07074040392218, + 12.051994367230447, + 12.566357609488715, + 12.635766032787924, + 14.039795745007444, + 14.753683213891955, + 12.798910382279683, + 13.141530914038844, + 14.554772877953402, + 17.38883089768602, + 23.365327383405454, + 4.74291210157339, + 15.491603946203218 ] }, "mode": "markers+text", @@ -2524,64 +1429,18 @@ " SELECT", " age", ",", + " vote", + ",", " zip", "code", - " FROM", - " data", - " WHERE", + ",", " vote", - " <", - " 50", - " 100", - " ", + " ", + "▪" ], "type": "scatter", "x": [ @@ -2597,104 +1456,12 @@ 9, 10, 11, - 12, - 12, 13, 14, 15, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 26, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 19 ], "y": [ - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 10, - 11, - 6, - 6, - 6, - 6, - 6, - 5, - 6, - 6, - 6, - 6, - 6, - 3, - 4, - 5, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 6, - 9, - 6, - 6, 6, 6, 6, @@ -2718,131 +1485,41 @@ "hovertext": [ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 7; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 7; Weight = -0.215239
", - "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 7; Weight = -0.796165
", - "Token: ` FROM`
Context: SELECT vote
Step 3; Avg weight = -0.963722
Particle 7; Weight = -1.548458
", - "Token: ` data`
Context: SELECT vote FROM
Step 4; Avg weight = -1.175300
Particle 7; Weight = -1.900072
", - "Token: ` ORDER`
Context: SELECT vote FROM data
Step 5; Avg weight = -1.228179
Particle 7; Weight = -2.126363
", - "Token: ` BY`
Context: SELECT vote FROM data ORDER
Step 6; Avg weight = -1.345798
Particle 7; Weight = -2.127632
", - "Token: ` age`
Context: SELECT vote FROM data ORDER BY
Step 7; Avg weight = -1.463050
Particle 7; Weight = -2.280870
", - "Token: ` ASC`
Context: SELECT vote FROM data ORDER BY age
Step 8; Avg weight = -1.561001
Particle 7; Weight = -3.039202
", - "Token: ` `
Context: SELECT vote FROM data ORDER BY age ASC
Step 9; Avg weight = -1.799961
Particle 7; Weight = -4.569391
", - "Token: `s`
Context: SELECT vote FROM data ORDER BY age ASC Step 10; Avg weight = -2.215472
Particle 7; Weight = -4.570369
", - "Token: `>`
Context: SELECT vote FROM data ORDER BY age ASC Step 11; Avg weight = -2.579298
Particle 7; Weight = -4.573563
", - "Token: ` BY`
Context: SELECT vote, age, zipcode, vote FROM data ORDER
Step 13; Avg weight = -3.675509
Particle 7; Weight = -3.333856
", - "Token: ` age`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY
Step 14; Avg weight = -4.248732
Particle 7; Weight = -3.462345
", - "Token: ` ASC`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age
Step 15; Avg weight = -4.755635
Particle 7; Weight = -4.066998
", - "Token: `245`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545
Step 18; Avg weight = -6.735819
Particle 7; Weight = -6.503222
", - "Token: `65`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245
Step 19; Avg weight = -7.171824
Particle 7; Weight = -6.556255
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454546 Step 20; Avg weight = -7.474923
Particle 6; Weight = -7.715015
↳ resampled as particle 7", - "Token: `1966`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565
Step 20; Avg weight = -7.474923
Particle 7; Weight = -6.621603
↳ resampled as particle 7", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545 Step 20; Avg weight = -7.474923
Particle 8; Weight = -15.914297
↳ resampled as particle 7", - "Token: `D`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456311 OR
Step 20; Avg weight = -7.474923
Particle 9; Weight = -22.796198
↳ resampled as particle 7", - "Token: `225`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966
Step 21; Avg weight = -7.750867
Particle 7; Weight = -7.576239
", - "Token: `349`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225
Step 22; Avg weight = -8.015190
Particle 7; Weight = -7.627956
", - "Token: `207`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349
Step 23; Avg weight = -8.109812
Particle 7; Weight = -7.735854
", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207
Step 24; Avg weight = -8.338520
Particle 7; Weight = -7.787099
", - "Token: `37`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745
Step 25; Avg weight = -8.503752
Particle 7; Weight = -7.827416
", - "Token: `77`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566
Step 26; Avg weight = -8.627681
Particle 6; Weight = -8.074892
↳ resampled as particle 7", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537
Step 26; Avg weight = -8.627681
Particle 7; Weight = -7.860385
↳ resampled as particle 7", - "Token: `49`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789
Step 27; Avg weight = -8.744810
Particle 7; Weight = -8.672037
", - "Token: `15`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378949
Step 28; Avg weight = -8.810626
Particle 7; Weight = -8.719932
", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537894915
Step 29; Avg weight = -8.849632
Particle 7; Weight = -8.756490
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 30; Avg weight = -8.994838
Particle 7; Weight = -10.416310
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589 Step 31; Avg weight = -9.029883
Particle 7; Weight = -10.421740
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589 Step 32; Avg weight = -9.058807
Particle 7; Weight = -10.435840
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 33; Avg weight = -9.120276
Particle 7; Weight = -15.416427
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 34; Avg weight = -9.147265
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 35; Avg weight = -9.236098
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 36; Avg weight = -9.379450
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 37; Avg weight = -9.395129
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 38; Avg weight = -9.434781
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 39; Avg weight = -9.618847
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 40; Avg weight = -9.650162
Particle 7; Weight = -23.134801
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789491589
Step 41; Avg weight = -9.792840
Particle 7; Weight = -23.134801
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064
Step 43; Avg weight = -9.894529
Particle 7; Weight = -9.894409
", - "Token: `25`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470
Step 44; Avg weight = -9.910677
Particle 7; Weight = -9.908270
", - "Token: `87`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470647025
Step 45; Avg weight = -9.921427
Particle 7; Weight = -9.925196
", - "Token: `26`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587
Step 46; Avg weight = -9.999674
Particle 7; Weight = -9.937650
", - "Token: `500`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470258726
Step 47; Avg weight = -10.018553
Particle 7; Weight = -9.946724
", - "Token: `985`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470258726500
Step 48; Avg weight = -10.029880
Particle 7; Weight = -9.956060
", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470258726500985
Step 49; Avg weight = -10.149053
Particle 7; Weight = -9.962630
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470647025872650098545
Step 50; Avg weight = -10.160593
Particle 7; Weight = -9.973644
", - "Token: `96`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587265009854510
Step 51; Avg weight = -10.174623
Particle 7; Weight = -9.985406
", - "Token: `75`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470258726500985451096
Step 52; Avg weight = -10.181790
Particle 7; Weight = -9.997855
", - "Token: `100`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470647025872650098545109675
Step 53; Avg weight = -10.191848
Particle 7; Weight = -10.009380
", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470647025872650098545109675100
Step 54; Avg weight = -10.225102
Particle 7; Weight = -10.018855
", - "Token: `364`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587265009854510967510050
Step 55; Avg weight = -10.231485
Particle 7; Weight = -10.028014
", - "Token: `266`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587265009854510967510050364
Step 56; Avg weight = -10.237751
Particle 7; Weight = -10.031030
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587265009854510967510050364266
Step 57; Avg weight = -10.244302
Particle 7; Weight = -10.039293
", - "Token: `98`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706470258726500985451096751005036426610
Step 58; Avg weight = -10.249955
Particle 7; Weight = -10.047132
", - "Token: `87`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470647025872650098545109675100503642661098
Step 59; Avg weight = -10.255388
Particle 7; Weight = -10.052547
", - "Token: `139`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064702587265009854510967510050364266109887
Step 60; Avg weight = -10.262210
Particle 7; Weight = -10.062443
" + "Token: ` stat`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 7; Weight = -2.683718
", + "Token: `e`
Context: SELECT stat
Step 3; Avg weight = -0.888091
Particle 7; Weight = -10.126488
", + "Token: `_`
Context: SELECT state
Step 4; Avg weight = -1.124863
Particle 7; Weight = -11.494895
", + "Token: `color`
Context: SELECT state_
Step 5; Avg weight = -1.283818
Particle 7; Weight = -11.870916
", + "Token: ` FROM`
Context: SELECT state_color
Step 6; Avg weight = -1.382515
Particle 7; Weight = -12.063396
", + "Token: ` data`
Context: SELECT state_color FROM
Step 7; Avg weight = -1.593709
Particle 7; Weight = -12.470836
", + "Token: ` WHERE`
Context: SELECT state_color FROM data
Step 8; Avg weight = -1.701664
Particle 7; Weight = -12.689535
", + "Token: ` race`
Context: SELECT state_color FROM data WHERE
Step 9; Avg weight = -1.831096
Particle 7; Weight = -13.543049
", + "Token: `_`
Context: SELECT state_color FROM data WHERE race
Step 10; Avg weight = -1.957496
Particle 7; Weight = -14.086808
", + "Token: `ethnic`
Context: SELECT state_color FROM data WHERE race_
Step 11; Avg weight = -2.368902
Particle 7; Weight = -14.100870
", + "Token: `s`
Context: SELECT vote, zipcode FROM data WHERE age >50 Step 13; Avg weight = -3.233764
Particle 7; Weight = -2.820425
", + "Token: `>`
Context: SELECT vote, zipcode FROM data WHERE age >50 Step 14; Avg weight = -3.830131
Particle 7; Weight = -2.825942
", + "Token: ` `
Context: SELECT vote, zipcode FROM data WHERE age >50
Step 15; Avg weight = -5.379061
Particle 7; Weight = -7.564076
", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 7; Weight = -8.748671
" ], "marker": { "color": "rgb(92.72727272727272, 79.54545454545455, 162.27272727272728)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, - 14.426875606340475, - 10.557018399945658, - 9.843121043369914, - 9.025619165916124, - 9.56625752823055, - 9.395664972338915, - 6.753482314837782, - 3.5411221096150953, - 4.356680810383481, - 5.217540232989122, - 16.776604387241186, - 20.954474671216666, - 19.954955386798584, - 15.88630636524312, - 19.239073252202257, - 12.542370580401576, - 21.667606568648715, - 0.20793532688971555, - 0.0066610439208297754, - 15.432451984784649, - 17.163339317342093, - 17.049783109553072, - 18.631788473628273, - 19.832599213516698, - 18.64453725209621, - 20.75540783342679, - 14.66619713174516, - 14.79820205952323, - 14.81632992128038, - 6.94778515817959, - 7.051426539663703, - 7.103886301469617, - 0.6071877807943628, - 0.01297657967534012, - 0.01356594485148302, - 0.014573988743692373, - 0.014688696570096272, - 0.014982819687555665, - 0.016427178286685235, - 0.01668640653951518, - 0.017920285588995045, - 14.142980626825556, - 14.159164537035474, - 14.115503302571938, - 14.587580458815111, - 14.65927404083039, - 14.673877347443783, - 15.523731642795145, - 15.527819831992876, - 15.545437975461532, - 15.504430469241658, - 15.493063339555412, - 15.678374013947456, - 15.656625423934717, - 15.682081673838706, - 15.668672112225336, - 15.65155900447242, - 15.651693685727343, - 15.627653896032212 + 14.142135623730951, + 14.142135623730951, + 5.614296315237451, + 0.1394513018334148, + 0.07919378997611413, + 0.07104867591533891, + 0.0677939706043754, + 0.06145776510785976, + 0.05814722678918151, + 0.0404852363392362, + 0.03286005741973027, + 0.04008209260671689, + 17.38883089768602, + 23.365327383405454, + 4.74291210157339, + 15.491603946203218 ] }, "mode": "markers+text", @@ -2850,65 +1527,20 @@ "text": [ [], " SELECT", - " vote", + " stat", + "e", + "_", + "color", " FROM", " data", - " ORDER", - " BY", - " age", - " ASC", - " ", - " BY", - " age", - " ASC", - "245", - "65", - "s", - "1966", - "s", - "D", - "225", - "349", - "207", - "45", - "37", - "77", - "89", - "49", - "15", - "89", - " ", " ", - "▪", - "▪", - "▪", - "▪", - "▪", - "▪", - "▪", - "▪", - "70", - "25", - "87", - "26", - "500", - "985", - "45", - "10", - "96", - "75", - "100", - "50", - "364", - "266", - "10", - "98", - "87", - "139" + "▪" ], "type": "scatter", "x": [ @@ -2927,99 +1559,9 @@ 13, 14, 15, - 18, - 19, - 20, - 20, - 20, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 19 ], "y": [ - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 6, - 7, - 8, - 9, - 7, - 7, - 7, - 7, - 7, - 6, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, - 7, 7, 7, 7, @@ -3043,131 +1585,45 @@ "hovertext": [ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 8; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 8; Weight = -0.215239
", - "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 8; Weight = -0.796165
", - "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.963722
Particle 8; Weight = -0.851550
", - "Token: ` age`
Context: SELECT vote,
Step 4; Avg weight = -1.175300
Particle 8; Weight = -0.983349
", - "Token: ` FROM`
Context: SELECT vote, age
Step 5; Avg weight = -1.228179
Particle 8; Weight = -1.085545
", - "Token: ` data`
Context: SELECT vote, age FROM
Step 6; Avg weight = -1.345798
Particle 8; Weight = -1.372316
", - "Token: ` ORDER`
Context: SELECT vote, age FROM data
Step 7; Avg weight = -1.463050
Particle 8; Weight = -1.535216
", - "Token: ` BY`
Context: SELECT vote, age FROM data ORDER
Step 8; Avg weight = -1.561001
Particle 8; Weight = -1.536064
", - "Token: ` age`
Context: SELECT vote, age FROM data ORDER BY
Step 9; Avg weight = -1.799961
Particle 8; Weight = -1.648144
", - "Token: ` DES`
Context: SELECT vote, age FROM data ORDER BY age
Step 10; Avg weight = -2.215472
Particle 8; Weight = -2.320734
", - "Token: `C`
Context: SELECT vote, age FROM data ORDER BY age DES
Step 11; Avg weight = -2.579298
Particle 8; Weight = -2.324008
", - "Token: ` BY`
Context: SELECT vote, age, zipcode, vote FROM data ORDER
Step 13; Avg weight = -3.675509
Particle 8; Weight = -3.333856
", - "Token: ` vote`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY
Step 14; Avg weight = -4.248732
Particle 8; Weight = -3.692969
", - "Token: ` DES`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY vote
Step 15; Avg weight = -4.755635
Particle 8; Weight = -4.138167
", - "Token: `>`
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC Step 16; Avg weight = -5.060239
Particle 4; Weight = -5.169872
↳ resampled as particle 8", - "Token: ` <`
Context: SELECT vote, zipcode, age FROM data ORDER BY age, vote ASC
Step 16; Avg weight = -5.060239
Particle 5; Weight = -8.220290
↳ resampled as particle 8", - "Token: ` <`
Context: SELECT vote, zipcode, age FROM data ORDER BY age, age ASC
Step 16; Avg weight = -5.060239
Particle 6; Weight = -7.512483
↳ resampled as particle 8", - "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -6.397758
Particle 0; Weight = -8.769549
↳ resampled as particle 8", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545
Step 18; Avg weight = -6.735819
Particle 8; Weight = -7.786469
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545 <
Step 19; Avg weight = -7.171824
Particle 8; Weight = -15.906079
", - "Token: `82`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966
Step 21; Avg weight = -7.750867
Particle 8; Weight = -7.576239
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196682
Step 22; Avg weight = -8.015190
Particle 8; Weight = -9.295110
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196682 <
Step 23; Avg weight = -8.109812
Particle 8; Weight = -17.510597
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196682 Step 24; Avg weight = -8.338520
Particle 8; Weight = -17.517747
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196682 Step 25; Avg weight = -8.503752
Particle 8; Weight = -17.531775
", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923
Step 27; Avg weight = -8.744810
Particle 8; Weight = -8.674175
", - "Token: `807`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364
Step 28; Avg weight = -8.810626
Particle 8; Weight = -8.721928
", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807
Step 29; Avg weight = -8.849632
Particle 8; Weight = -8.816711
", - "Token: `116`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764
Step 30; Avg weight = -8.994838
Particle 8; Weight = -8.862452
", - "Token: `60`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116
Step 31; Avg weight = -9.029883
Particle 8; Weight = -8.908375
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660
Step 32; Avg weight = -9.058807
Particle 8; Weight = -8.936061
", - "Token: `87`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030
Step 33; Avg weight = -9.120276
Particle 8; Weight = -8.961780
", - "Token: `14`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087
Step 34; Avg weight = -9.147265
Particle 8; Weight = -8.984197
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714
Step 35; Avg weight = -9.236098
Particle 8; Weight = -9.004205
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413
Step 36; Avg weight = -9.379450
Particle 8; Weight = -9.024799
", - "Token: `31`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399
Step 37; Avg weight = -9.395129
Particle 8; Weight = -9.036963
", - "Token: `80`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931
Step 38; Avg weight = -9.434781
Particle 8; Weight = -9.156915
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180
Step 39; Avg weight = -9.618847
Particle 8; Weight = -9.172499
", - "Token: `98`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047
Step 40; Avg weight = -9.650162
Particle 8; Weight = -9.187763
", - "Token: `950`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798
Step 41; Avg weight = -9.792840
Particle 8; Weight = -9.204346
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208
Step 42; Avg weight = -9.884456
Particle 10; Weight = -29.902105
↳ resampled as particle 8", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617160603639882 GROUP BY vote
Step 42; Avg weight = -9.884456
Particle 11; Weight = -12.981168
↳ resampled as particle 8", - "Token: `48`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064
Step 43; Avg weight = -9.894529
Particle 8; Weight = -9.894409
", - "Token: `67`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448
Step 44; Avg weight = -9.910677
Particle 8; Weight = -9.906084
", - "Token: `27`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470644867
Step 45; Avg weight = -9.921427
Particle 8; Weight = -9.926986
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727
Step 46; Avg weight = -9.999674
Particle 8; Weight = -9.940977
", - "Token: `901`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448672770
Step 47; Avg weight = -10.018553
Particle 8; Weight = -9.951524
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448672770901
Step 48; Avg weight = -10.029880
Particle 8; Weight = -9.974785
", - "Token: `93`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470644867277090113
Step 49; Avg weight = -10.149053
Particle 8; Weight = -9.985857
", - "Token: `229`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393
Step 50; Avg weight = -10.160593
Particle 8; Weight = -10.004066
", - "Token: `595`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229
Step 51; Avg weight = -10.174623
Particle 8; Weight = -10.016096
", - "Token: `153`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229595
Step 52; Avg weight = -10.181790
Particle 8; Weight = -10.035520
", - "Token: `249`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229595153
Step 53; Avg weight = -10.191848
Particle 8; Weight = -10.065453
", - "Token: `08`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229595153249
Step 54; Avg weight = -10.225102
Particle 8; Weight = -10.074809
", - "Token: `9`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448672770901139322959515324908
Step 55; Avg weight = -10.231485
Particle 8; Weight = -10.082152
", - "Token: `77`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229595153249089
Step 56; Avg weight = -10.237751
Particle 8; Weight = -10.094498
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448672770901139322959515324908977
Step 57; Avg weight = -10.244302
Particle 8; Weight = -10.102072
", - "Token: `78`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519666042922456677401313471601773649064077031576223470644867277090113932295951532490897710
Step 58; Avg weight = -10.249955
Particle 8; Weight = -10.108738
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966604292245667740131347160177364906407703157622347064486727709011393229595153249089771078
Step 59; Avg weight = -10.255388
Particle 8; Weight = -10.112572
", - "Token: `80`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196660429224566774013134716017736490640770315762234706448672770901139322959515324908977107810
Step 60; Avg weight = -10.262210
Particle 8; Weight = -10.115548
" + "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 8; Weight = -0.734827
", + "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 8; Weight = -0.760898
", + "Token: ` gender`
Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 8; Weight = -0.996677
", + "Token: ` FROM`
Context: SELECT age, gender
Step 5; Avg weight = -1.283818
Particle 8; Weight = -1.037804
", + "Token: ` data`
Context: SELECT age, gender FROM
Step 6; Avg weight = -1.382515
Particle 8; Weight = -1.199354
", + "Token: ` WHERE`
Context: SELECT age, gender FROM data
Step 7; Avg weight = -1.593709
Particle 8; Weight = -1.308047
", + "Token: ` gender`
Context: SELECT age, gender FROM data WHERE
Step 8; Avg weight = -1.701664
Particle 8; Weight = -1.355293
", + "Token: `>`
Context: SELECT age, gender FROM data WHERE gender
Step 9; Avg weight = -1.831096
Particle 8; Weight = -1.390399
", + "Token: `50`
Context: SELECT age, gender FROM data WHERE gender>
Step 10; Avg weight = -1.957496
Particle 8; Weight = -1.415016
", + "Token: ` ORDER`
Context: SELECT age, gender FROM data WHERE gender>50
Step 11; Avg weight = -2.368902
Particle 8; Weight = -2.271291
", + "Token: ` BY`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER
Step 12; Avg weight = -2.817760
Particle 8; Weight = -2.272472
↳ resampled as particle 8", + "Token: ` age`
Context: SELECT age, zipcode, vote FROM data ORDER BY
Step 12; Avg weight = -2.817760
Particle 9; Weight = -2.261326
↳ resampled as particle 8", + "Token: ` age`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY
Step 13; Avg weight = -3.233764
Particle 8; Weight = -2.896389
", + "Token: ` AS`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY age
Step 14; Avg weight = -3.830131
Particle 8; Weight = -3.424770
", + "Token: `C`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY age AS
Step 15; Avg weight = -5.379061
Particle 8; Weight = -14.155769
", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 8; Weight = -8.748671
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Context:
Step 0; Avg weight = 0.000000
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", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 9; Weight = -0.215239
", "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 9; Weight = -0.734827
", - "Token: ` FROM`
Context: SELECT age
Step 3; Avg weight = -0.963722
Particle 9; Weight = -0.952327
", - "Token: ` data`
Context: SELECT age FROM
Step 4; Avg weight = -1.175300
Particle 9; Weight = -1.325075
", - "Token: ` WHERE`
Context: SELECT age FROM data
Step 5; Avg weight = -1.228179
Particle 9; Weight = -1.470265
", - "Token: ` age`
Context: SELECT age FROM data WHERE
Step 6; Avg weight = -1.345798
Particle 9; Weight = -1.611382
", - "Token: `>`
Context: SELECT age FROM data WHERE age
Step 7; Avg weight = -1.463050
Particle 9; Weight = -1.643539
", - "Token: `50`
Context: SELECT age FROM data WHERE age>
Step 8; Avg weight = -1.561001
Particle 9; Weight = -1.658446
", - "Token: ` <`
Context: SELECT age FROM data WHERE age>50
Step 9; Avg weight = -1.799961
Particle 9; Weight = -2.556337
", - "Token: `/`
Context: SELECT age FROM data WHERE age>50 <
Step 10; Avg weight = -2.215472
Particle 9; Weight = -8.621545
", - "Token: `s`
Context: SELECT age FROM data WHERE age>50 Step 11; Avg weight = -2.579298
Particle 9; Weight = -8.626785
", - "Token: ` BY`
Context: SELECT vote, age, zipcode, vote FROM data ORDER
Step 13; Avg weight = -3.675509
Particle 9; Weight = -3.333856
", - "Token: ` age`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY
Step 14; Avg weight = -4.248732
Particle 9; Weight = -3.462345
", - "Token: ` DES`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age
Step 15; Avg weight = -4.755635
Particle 9; Weight = -4.071161
", - "Token: ` `
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age ASC
Step 16; Avg weight = -5.060239
Particle 7; Weight = -5.418425
↳ resampled as particle 9", - "Token: `C`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY vote DES
Step 16; Avg weight = -5.060239
Particle 8; Weight = -4.141533
↳ resampled as particle 9", - "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -6.397758
Particle 1; Weight = -8.769549
↳ resampled as particle 9", - "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -6.397758
Particle 2; Weight = -8.769549
↳ resampled as particle 9", - "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -6.397758
Particle 3; Weight = -8.769549
↳ resampled as particle 9", - "Token: ` `
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY vote DESC
Step 17; Avg weight = -6.397758
Particle 4; Weight = -8.227623
↳ resampled as particle 9", - "Token: ` `
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY vote DESC
Step 17; Avg weight = -6.397758
Particle 5; Weight = -8.227623
↳ resampled as particle 9", - "Token: `11`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563
Step 18; Avg weight = -6.735819
Particle 9; Weight = -6.561730
", - "Token: ` OR`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456311
Step 19; Avg weight = -7.171824
Particle 9; Weight = -8.083433
", - "Token: `225`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966
Step 21; Avg weight = -7.750867
Particle 9; Weight = -7.576239
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Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225
Step 22; Avg weight = -8.015190
Particle 9; Weight = -7.627956
", - "Token: `31`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662255
Step 23; Avg weight = -8.109812
Particle 9; Weight = -7.683653
", - "Token: `25`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225531
Step 24; Avg weight = -8.338520
Particle 9; Weight = -8.064938
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622553125
Step 25; Avg weight = -8.503752
Particle 9; Weight = -9.467728
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923
Step 27; Avg weight = -8.744810
Particle 9; Weight = -8.674175
", - "Token: `401`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347
Step 28; Avg weight = -8.810626
Particle 9; Weight = -8.714671
", - "Token: `5`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401
Step 29; Avg weight = -8.849632
Particle 9; Weight = -8.735831
", - "Token: `64`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923474015
Step 30; Avg weight = -8.994838
Particle 9; Weight = -8.861687
", - "Token: `295`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401564
Step 31; Avg weight = -9.029883
Particle 9; Weight = -8.892373
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401564295
Step 32; Avg weight = -9.058807
Particle 9; Weight = -8.933321
", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369234740156429599
Step 33; Avg weight = -9.120276
Particle 9; Weight = -8.968316
", - "Token: `98`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923474015642959950
Step 34; Avg weight = -9.147265
Particle 9; Weight = -8.993696
", - "Token: `417`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401564295995098
Step 35; Avg weight = -9.236098
Particle 9; Weight = -9.019705
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Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401564295995098417
Step 36; Avg weight = -9.379450
Particle 9; Weight = -9.034042
", - "Token: `88`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369234740156429599509841735
Step 37; Avg weight = -9.395129
Particle 9; Weight = -9.050463
", - "Token: `49`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923474015642959950984173588
Step 38; Avg weight = -9.434781
Particle 9; Weight = -9.069986
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Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692347401564295995098417358849
Step 39; Avg weight = -9.618847
Particle 9; Weight = -9.091495
", - "Token: `05`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369234740156429599509841735884928
Step 40; Avg weight = -9.650162
Particle 9; Weight = -9.112070
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923474015642959950984173588492805
Step 41; Avg weight = -9.792840
Particle 9; Weight = -10.058984
", - "Token: `75`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349
Step 43; Avg weight = -9.894529
Particle 9; Weight = -9.891958
", - "Token: `11`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975
Step 44; Avg weight = -9.910677
Particle 9; Weight = -9.906497
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511
Step 45; Avg weight = -9.921427
Particle 9; Weight = -9.917175
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349751170
Step 46; Avg weight = -9.999674
Particle 9; Weight = -9.920113
", - "Token: `65`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975117047
Step 47; Avg weight = -10.018553
Particle 9; Weight = -9.924778
", - "Token: `186`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511704765
Step 48; Avg weight = -10.029880
Particle 9; Weight = -9.936783
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511704765186
Step 49; Avg weight = -10.149053
Particle 9; Weight = -9.948179
", - "Token: `78`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349751170476518613
Step 50; Avg weight = -10.160593
Particle 9; Weight = -9.955585
", - "Token: `46`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975117047651861378
Step 51; Avg weight = -10.174623
Particle 9; Weight = -9.960370
", - "Token: `20`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511704765186137846
Step 52; Avg weight = -10.181790
Particle 9; Weight = -9.968845
", - "Token: `96`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349751170476518613784620
Step 53; Avg weight = -10.191848
Particle 9; Weight = -9.972656
", - "Token: `13`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975117047651861378462096
Step 54; Avg weight = -10.225102
Particle 9; Weight = -9.981068
", - "Token: `10`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511704765186137846209613
Step 55; Avg weight = -10.231485
Particle 9; Weight = -9.985659
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Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349751170476518613784620961310
Step 56; Avg weight = -10.237751
Particle 9; Weight = -9.989473
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Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975117047651861378462096131027
Step 57; Avg weight = -10.244302
Particle 9; Weight = -9.996539
", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622534920745378950541652218183759354037726089216534975117047651861378462096131027164
Step 58; Avg weight = -10.249955
Particle 9; Weight = -10.001988
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Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945452456519662253492074537895054165221818375935403772608921653497511704765186137846209613102716445
Step 59; Avg weight = -10.255388
Particle 9; Weight = -10.009809
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Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494545245651966225349207453789505416522181837593540377260892165349751170476518613784620961310271644588
Step 60; Avg weight = -10.262210
Particle 9; Weight = -10.015003
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Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 9; Weight = -0.760898
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Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 9; Weight = -0.976939
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Context: SELECT age, zip
Step 5; Avg weight = -1.283818
Particle 9; Weight = -0.977817
", + "Token: `,`
Context: SELECT age, zipcode
Step 6; Avg weight = -1.382515
Particle 9; Weight = -0.984795
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Context: SELECT age, zipcode,
Step 7; Avg weight = -1.593709
Particle 9; Weight = -1.504912
", + "Token: ` FROM`
Context: SELECT age, zipcode, vote
Step 8; Avg weight = -1.701664
Particle 9; Weight = -1.834555
", + "Token: ` data`
Context: SELECT age, zipcode, vote FROM
Step 9; Avg weight = -1.831096
Particle 9; Weight = -2.016027
", + "Token: ` ORDER`
Context: SELECT age, zipcode, vote FROM data
Step 10; Avg weight = -1.957496
Particle 9; Weight = -2.148296
", + "Token: ` BY`
Context: SELECT age, zipcode, vote FROM data ORDER
Step 11; Avg weight = -2.368902
Particle 9; Weight = -2.149252
", + "Token: ` BY`
Context: SELECT age, gender FROM data WHERE gender>19 ORDER
Step 12; Avg weight = -2.817760
Particle 10; Weight = -2.717509
↳ resampled as particle 9", + "Token: ` `
Context: SELECT vote, zipcode FROM data ORDER BY age ASC
Step 12; Avg weight = -2.817760
Particle 11; Weight = -3.402973
↳ resampled as particle 9", + "Token: ` vote`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY
Step 13; Avg weight = -3.233764
Particle 9; Weight = -3.095843
", + "Token: ` ASC`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote
Step 14; Avg weight = -3.830131
Particle 9; Weight = -3.712254
", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 15; Avg weight = -5.379061
Particle 9; Weight = -4.934339
", + "Token: `/`
Context: SELECT age, zipcode, vote FROM data ORDER BY age DESC <
Step 16; Avg weight = -5.428199
Particle 10; Weight = -14.127563
↳ resampled as particle 9", + "Token: `s`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC Step 16; Avg weight = -5.428199
Particle 11; Weight = -11.205070
↳ resampled as particle 9", + "Token: `▪`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 19; Avg weight = -8.930951
Particle 9; Weight = -8.748671
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Step 0; Avg weight = 0.000000
Particle 10; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 10; Weight = -0.215239
", - "Token: ` state`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 10; Weight = -2.683718
", - "Token: `_`
Context: SELECT state
Step 3; Avg weight = -0.963722
Particle 10; Weight = -4.052125
", - "Token: `color`
Context: SELECT state_
Step 4; Avg weight = -1.175300
Particle 10; Weight = -4.428145
", - "Token: ` FROM`
Context: SELECT state_color
Step 5; Avg weight = -1.228179
Particle 10; Weight = -4.620626
", - "Token: ` data`
Context: SELECT state_color FROM
Step 6; Avg weight = -1.345798
Particle 10; Weight = -5.028066
", - "Token: ` WHERE`
Context: SELECT state_color FROM data
Step 7; Avg weight = -1.463050
Particle 10; Weight = -5.246765
", - "Token: ` state`
Context: SELECT state_color FROM data WHERE
Step 8; Avg weight = -1.561001
Particle 10; Weight = -5.424254
", - "Token: `_`
Context: SELECT state_color FROM data WHERE state
Step 9; Avg weight = -1.799961
Particle 10; Weight = -5.640223
", - "Token: `color`
Context: SELECT state_color FROM data WHERE state_
Step 10; Avg weight = -2.215472
Particle 10; Weight = -5.766550
", - "Token: ` >`
Context: SELECT state_color FROM data WHERE state_color
Step 11; Avg weight = -2.579298
Particle 10; Weight = -5.961112
", - "Token: `405`
Context: SELECT age, zipcode FROM data WHERE vote < 5020
Step 13; Avg weight = -3.675509
Particle 10; Weight = -3.598254
", - "Token: `153`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405
Step 14; Avg weight = -4.248732
Particle 10; Weight = -3.694007
", - "Token: `449`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153
Step 15; Avg weight = -4.755635
Particle 10; Weight = -3.810796
", - "Token: `C`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age DES
Step 16; Avg weight = -5.060239
Particle 9; Weight = -4.073812
↳ resampled as particle 10", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449
Step 16; Avg weight = -5.060239
Particle 10; Weight = -3.868798
↳ resampled as particle 10", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020
Step 16; Avg weight = -5.060239
Particle 11; Weight = -10.478288
↳ resampled as particle 10", - "Token: ` <`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY vote DESC
Step 17; Avg weight = -6.397758
Particle 6; Weight = -8.227623
↳ resampled as particle 10", - "Token: ` <`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age DESC
Step 17; Avg weight = -6.397758
Particle 7; Weight = -8.068334
↳ resampled as particle 10", - "Token: ` <`
Context: SELECT vote, age, zipcode, vote FROM data ORDER BY age DESC
Step 17; Avg weight = -6.397758
Particle 8; Weight = -8.068334
↳ resampled as particle 10", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563
Step 18; Avg weight = -6.735819
Particle 10; Weight = -7.969287
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563 <
Step 19; Avg weight = -7.171824
Particle 10; Weight = -15.703250
", - "Token: `17`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212
Step 21; Avg weight = -7.750867
Particle 10; Weight = -7.565560
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217
Step 22; Avg weight = -8.015190
Particle 10; Weight = -7.604170
", - "Token: `630`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799
Step 23; Avg weight = -8.109812
Particle 10; Weight = -7.648953
", - "Token: `90`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630
Step 24; Avg weight = -8.338520
Particle 10; Weight = -7.680736
", - "Token: `369`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090
Step 25; Avg weight = -8.503752
Particle 10; Weight = -7.744203
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196682
Step 26; Avg weight = -8.627681
Particle 8; Weight = -22.884492
↳ resampled as particle 10", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449454524565196622553125 <
Step 26; Avg weight = -8.627681
Particle 9; Weight = -16.955744
↳ resampled as particle 10", - "Token: `23`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369
Step 26; Avg weight = -8.627681
Particle 10; Weight = -7.800261
↳ resampled as particle 10", - "Token: `75`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923
Step 27; Avg weight = -8.744810
Particle 10; Weight = -8.674175
", - "Token: `95`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692375
Step 28; Avg weight = -8.810626
Particle 10; Weight = -8.731108
", - "Token: `95`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369237595
Step 29; Avg weight = -8.849632
Particle 10; Weight = -8.799933
", - "Token: `177`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595
Step 30; Avg weight = -8.994838
Particle 10; Weight = -8.879800
", - "Token: `37`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177
Step 31; Avg weight = -9.029883
Particle 10; Weight = -8.924596
", - "Token: `28`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692375959517737
Step 32; Avg weight = -9.058807
Particle 10; Weight = -8.960110
", - "Token: `83`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369237595951773728
Step 33; Avg weight = -9.120276
Particle 10; Weight = -8.985912
", - "Token: `208`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883
Step 34; Avg weight = -9.147265
Particle 10; Weight = -9.027638
", - "Token: ` <`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208
Step 35; Avg weight = -9.236098
Particle 10; Weight = -10.083558
", - "Token: `/`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208 <
Step 36; Avg weight = -9.379450
Particle 10; Weight = -17.380099
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208 Step 37; Avg weight = -9.395129
Particle 10; Weight = -17.382984
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208 Step 38; Avg weight = -9.434781
Particle 10; Weight = -17.394164
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208
Step 39; Avg weight = -9.618847
Particle 10; Weight = -22.200106
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208
Step 40; Avg weight = -9.650162
Particle 10; Weight = -29.902105
", - "Token: `▪`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923759595177372883208
Step 41; Avg weight = -9.792840
Particle 10; Weight = -29.902105
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465
Step 43; Avg weight = -9.894529
Particle 10; Weight = -9.896227
", - "Token: `49`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547
Step 44; Avg weight = -9.910677
Particle 10; Weight = -9.908315
", - "Token: `089`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504654749
Step 45; Avg weight = -9.921427
Particle 10; Weight = -9.924214
", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504654749089
Step 46; Avg weight = -9.999674
Particle 10; Weight = -9.946416
", - "Token: `5`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465474908950
Step 47; Avg weight = -10.018553
Particle 10; Weight = -9.952126
", - "Token: `47`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504654749089505
Step 48; Avg weight = -10.029880
Particle 10; Weight = -9.975927
", - "Token: `42`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465474908950547
Step 49; Avg weight = -10.149053
Particle 10; Weight = -9.986824
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742
Step 50; Avg weight = -10.160593
Particle 10; Weight = -10.012187
", - "Token: `30`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504654749089505474212
Step 51; Avg weight = -10.174623
Particle 10; Weight = -10.019792
", - "Token: `95`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465474908950547421230
Step 52; Avg weight = -10.181790
Particle 10; Weight = -10.022752
", - "Token: `430`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742123095
Step 53; Avg weight = -10.191848
Particle 10; Weight = -10.027524
", - "Token: `260`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742123095430
Step 54; Avg weight = -10.225102
Particle 10; Weight = -10.029300
", - "Token: `617`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742123095430260
Step 55; Avg weight = -10.231485
Particle 10; Weight = -10.033706
", - "Token: `650`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742123095430260617
Step 56; Avg weight = -10.237751
Particle 10; Weight = -10.038107
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046547490895054742123095430260617650
Step 57; Avg weight = -10.244302
Particle 10; Weight = -10.040920
", - "Token: `70`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504654749089505474212309543026061765070
Step 58; Avg weight = -10.249955
Particle 10; Weight = -10.044236
", - "Token: `750`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465474908950547421230954302606176507070
Step 59; Avg weight = -10.255388
Particle 10; Weight = -10.048711
", - "Token: `820`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465474908950547421230954302606176507070750
Step 60; Avg weight = -10.262210
Particle 10; Weight = -10.052275
" + "Token: ` age`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 10; Weight = -0.734827
", + "Token: `,`
Context: SELECT age
Step 3; Avg weight = -0.888091
Particle 10; Weight = -0.760898
", + "Token: ` gender`
Context: SELECT age,
Step 4; Avg weight = -1.124863
Particle 10; Weight = -0.996677
", + "Token: ` FROM`
Context: SELECT age, gender
Step 5; Avg weight = -1.283818
Particle 10; Weight = -1.037804
", + "Token: ` data`
Context: SELECT age, gender FROM
Step 6; Avg weight = -1.382515
Particle 10; Weight = -1.199354
", + "Token: ` WHERE`
Context: SELECT age, gender FROM data
Step 7; Avg weight = -1.593709
Particle 10; Weight = -1.308047
", + "Token: ` gender`
Context: SELECT age, gender FROM data WHERE
Step 8; Avg weight = -1.701664
Particle 10; Weight = -1.355293
", + "Token: `>`
Context: SELECT age, gender FROM data WHERE gender
Step 9; Avg weight = -1.831096
Particle 10; Weight = -1.390399
", + "Token: `19`
Context: SELECT age, gender FROM data WHERE gender>
Step 10; Avg weight = -1.957496
Particle 10; Weight = -1.415016
", + "Token: ` ORDER`
Context: SELECT age, gender FROM data WHERE gender>19
Step 11; Avg weight = -2.368902
Particle 10; Weight = -2.714540
", + "Token: ` DES`
Context: SELECT age, zipcode, vote FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 10; Weight = -3.475490
", + "Token: `C`
Context: SELECT age, zipcode, vote FROM data ORDER BY age DES
Step 14; Avg weight = -3.830131
Particle 10; Weight = -3.478531
", + "Token: ` <`
Context: SELECT age, zipcode, vote FROM data ORDER BY age DESC
Step 15; Avg weight = -5.379061
Particle 10; Weight = -6.512657
", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 3; Weight = -9.179713
↳ resampled as particle 10", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 4; Weight = -9.179713
↳ resampled as particle 10", + "Token: ` `
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 5; Weight = -9.179713
↳ resampled as particle 10", + "Token: ` `
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 18; Avg weight = -8.748671
Particle 11; Weight = -10.709838
↳ resampled as particle 10", + "Token: `▪`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 19; Avg weight = -8.930951
Particle 10; Weight = -17.204494
" ], "marker": { "color": "rgb(23.18181818181816, 113.63636363636363, 231.81818181818184)", "opacity": 0.15, "size": [ - 14.142135623730953, - 14.142135623730953, - 5.614296315237451, - 3.0190953496501858, - 2.78079226840757, - 2.593310690086125, - 2.243471961821113, - 2.1325130401282184, - 2.0493695021669835, - 2.0730634586583436, - 2.3955672500429106, - 2.607134282865227, - 14.699103049866618, - 18.662597397384783, - 22.682152938356904, - 23.158742466123805, - 25.658613224827025, - 0.941890893365728, - 5.664542737841267, - 6.134142675957685, - 6.134142675957685, - 7.632571310962754, - 0.1985817880180337, - 15.515071969005827, - 17.368688693537624, - 17.806936570150086, - 19.6494767901248, - 20.675167036153525, - 0.01134195583492878, - 0.2198361557259388, - 21.388827694291994, - 14.650529610458651, - 14.71573704809776, - 14.49796353953173, - 14.9794258358236, - 14.906572820243714, - 14.857530334903126, - 15.12487577870506, - 15.013839821459634, - 9.257450148115971, - 0.2589380996429751, - 0.26059996257566936, - 0.26433641597045765, - 0.026213758870338468, - 0.0005660585722951952, - 0.0006079158656243207, - 14.13012961361189, - 14.158844713472917, - 14.12244188890075, - 14.523784480034328, - 14.619734908801538, - 14.528833328275086, - 15.337074268736604, - 15.231439980474606, - 15.28045322879757, - 15.31262447126814, - 15.35315224586664, - 15.596706158382965, - 15.612132971604709, - 15.62669556924604, - 15.655932554282357, - 15.674235790117883, - 15.681741801792516, - 15.707305615263472 + 14.142135623730951, + 14.142135623730951, + 14.876188905320037, + 15.07074040392218, + 15.078224808893935, + 15.993234241881277, + 15.498436940803993, + 16.313446866873942, + 16.81621851217536, + 17.628332308322847, + 18.548686335863405, + 11.897633404000542, + 12.532131299076044, + 16.860253688456698, + 8.023390958884487, + 4.92127331385489, + 4.92127331385489, + 4.92127331385489, + 5.304604544335504, + 0.22591119915659127 ] }, "mode": "markers+text", @@ -3836,68 +1865,24 @@ "text": [ [], " SELECT", - " state", - "_", - "color", + " age", + ",", + " gender", " FROM", " data", " WHERE", - " state", - "_", - "color", - " >", - "405", - "153", - "449", + " gender", + ">", + "19", + " ORDER", + " DES", "C", - "45", - " ", - " <", - " <", - " <", " <", - "/", - "17", - "99", - "630", - "90", - "369", " ", - "/", - "23", - "75", - "95", - "95", - "177", - "37", - "28", - "83", - "208", - " <", - "/", - "s", - ">", " ", - "▪", - "▪", - "47", - "49", - "089", - "50", - "5", - "47", - "42", - "12", - "30", - "95", - "430", - "260", - "617", - "650", - "70", - "70", - "750", - "820" + " ", + " ", + "▪" ], "type": "scatter", "x": [ @@ -3916,55 +1901,11 @@ 13, 14, 15, - 16, - 16, - 16, 17, 17, 17, 18, - 19, - 21, - 22, - 23, - 24, - 25, - 26, - 26, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, - 57, - 58, - 59, - 60 + 19 ], "y": [ 10, @@ -3982,54 +1923,10 @@ 10, 10, 10, - 9, - 10, + 3, + 4, + 5, 11, - 6, - 7, - 8, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 8, - 9, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, - 10, 10 ] }, @@ -4039,130 +1936,52 @@ "Token:
Context:
Step 0; Avg weight = 0.000000
Particle 11; Weight = 0.000000
", "Token: ` SELECT`
Context:
Step 1; Avg weight = -0.215239
Particle 11; Weight = -0.215239
", "Token: ` vote`
Context: SELECT
Step 2; Avg weight = -0.836033
Particle 11; Weight = -0.796165
", - "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.963722
Particle 11; Weight = -0.851550
", - "Token: ` zip`
Context: SELECT vote,
Step 4; Avg weight = -1.175300
Particle 11; Weight = -0.941903
", - "Token: `code`
Context: SELECT vote, zip
Step 5; Avg weight = -1.228179
Particle 11; Weight = -0.942447
", - "Token: ` FROM`
Context: SELECT vote, zipcode
Step 6; Avg weight = -1.345798
Particle 11; Weight = -1.031305
", - "Token: ` data`
Context: SELECT vote, zipcode FROM
Step 7; Avg weight = -1.463050
Particle 11; Weight = -1.193927
", - "Token: ` ORDER`
Context: SELECT vote, zipcode FROM data
Step 8; Avg weight = -1.561001
Particle 11; Weight = -1.311960
", - "Token: ` BY`
Context: SELECT vote, zipcode FROM data ORDER
Step 9; Avg weight = -1.799961
Particle 11; Weight = -1.312752
", - "Token: ` age`
Context: SELECT vote, zipcode FROM data ORDER BY
Step 10; Avg weight = -2.215472
Particle 11; Weight = -1.439440
", - "Token: ` ASC`
Context: SELECT vote, zipcode FROM data ORDER BY age
Step 11; Avg weight = -2.579298
Particle 11; Weight = -2.069171
", - "Token: ` `
Context: SELECT age, zipcode FROM data WHERE vote < 5020
Step 13; Avg weight = -3.675509
Particle 11; Weight = -4.751565
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 5020 Step 14; Avg weight = -4.248732
Particle 11; Weight = -4.761682
", - "Token: `>`
Context: SELECT age, zipcode FROM data WHERE vote < 5020 Step 15; Avg weight = -4.755635
Particle 11; Weight = -4.774241
", - "Token: `66`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945
Step 17; Avg weight = -6.397758
Particle 9; Weight = -5.119683
↳ resampled as particle 11", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945
Step 17; Avg weight = -6.397758
Particle 10; Weight = -5.119683
↳ resampled as particle 11", - "Token: `63`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945
Step 17; Avg weight = -6.397758
Particle 11; Weight = -5.119683
↳ resampled as particle 11", - "Token: `50`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563
Step 18; Avg weight = -6.735819
Particle 11; Weight = -6.561730
", - "Token: `42`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350
Step 19; Avg weight = -7.171824
Particle 11; Weight = -6.682405
", - "Token: `s`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563 Step 20; Avg weight = -7.474923
Particle 10; Weight = -15.713323
↳ resampled as particle 11", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042
Step 20; Avg weight = -7.474923
Particle 11; Weight = -6.914130
↳ resampled as particle 11", - "Token: `76`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212
Step 21; Avg weight = -7.750867
Particle 11; Weight = -7.565560
", - "Token: `33`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421276
Step 22; Avg weight = -8.015190
Particle 11; Weight = -7.616539
", - "Token: `67`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633
Step 23; Avg weight = -8.109812
Particle 11; Weight = -7.670015
", - "Token: `46`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367
Step 24; Avg weight = -8.338520
Particle 11; Weight = -7.728990
", - "Token: `16`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421276336746
Step 25; Avg weight = -8.503752
Particle 11; Weight = -7.774207
", - "Token: `320`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616
Step 26; Avg weight = -8.627681
Particle 11; Weight = -7.828663
↳ resampled as particle 11", - "Token: `9`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320
Step 27; Avg weight = -8.744810
Particle 11; Weight = -8.666655
", - "Token: `269`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421276336746163209
Step 28; Avg weight = -8.810626
Particle 11; Weight = -8.817180
", - "Token: `08`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421276336746163209269
Step 29; Avg weight = -8.849632
Particle 11; Weight = -8.855143
", - "Token: `401`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908
Step 30; Avg weight = -8.994838
Particle 11; Weight = -8.889815
", - "Token: `194`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908401
Step 31; Avg weight = -9.029883
Particle 11; Weight = -8.909277
", - "Token: `196`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908401194
Step 32; Avg weight = -9.058807
Particle 11; Weight = -8.952414
", - "Token: `17`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908401194196
Step 33; Avg weight = -9.120276
Particle 11; Weight = -8.991261
", - "Token: `160`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617
Step 34; Avg weight = -9.147265
Particle 11; Weight = -9.022746
", - "Token: `60`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617160
Step 35; Avg weight = -9.236098
Particle 11; Weight = -9.052762
", - "Token: `36`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421276336746163209269084011941961716060
Step 36; Avg weight = -9.379450
Particle 11; Weight = -9.079968
", - "Token: `398`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908401194196171606036
Step 37; Avg weight = -9.395129
Particle 11; Weight = -9.100340
", - "Token: `82`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042127633674616320926908401194196171606036398
Step 38; Avg weight = -9.434781
Particle 11; Weight = -9.120088
", - "Token: ` GROUP`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617160603639882
Step 39; Avg weight = -9.618847
Particle 11; Weight = -11.164320
", - "Token: ` BY`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617160603639882 GROUP
Step 40; Avg weight = -9.650162
Particle 11; Weight = -11.174407
", - "Token: ` vote`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212763367461632092690840119419617160603639882 GROUP BY
Step 41; Avg weight = -9.792840
Particle 11; Weight = -11.602822
", - "Token: `118`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465
Step 43; Avg weight = -9.894529
Particle 11; Weight = -9.896227
", - "Token: `27`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118
Step 44; Avg weight = -9.910677
Particle 11; Weight = -9.905038
", - "Token: `45`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046511827
Step 45; Avg weight = -9.921427
Particle 11; Weight = -9.916649
", - "Token: `272`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504651182745
Step 46; Avg weight = -9.999674
Particle 11; Weight = -9.925907
", - "Token: `12`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504651182745272
Step 47; Avg weight = -10.018553
Particle 11; Weight = -10.055570
", - "Token: `299`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212
Step 48; Avg weight = -10.029880
Particle 11; Weight = -10.061811
", - "Token: `44`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212299
Step 49; Avg weight = -10.149053
Particle 11; Weight = -10.069507
", - "Token: `224`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046511827452721229944
Step 50; Avg weight = -10.160593
Particle 11; Weight = -10.078301
", - "Token: `34`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046511827452721229944224
Step 51; Avg weight = -10.174623
Particle 11; Weight = -10.083168
", - "Token: `89`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504651182745272122994422434
Step 52; Avg weight = -10.181790
Particle 11; Weight = -10.089538
", - "Token: `970`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212299442243489
Step 53; Avg weight = -10.191848
Particle 11; Weight = -10.098319
", - "Token: `34`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212299442243489970
Step 54; Avg weight = -10.225102
Particle 11; Weight = -10.102145
", - "Token: `99`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046511827452721229944224348997034
Step 55; Avg weight = -10.231485
Particle 11; Weight = -10.106177
", - "Token: `610`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504651182745272122994422434899703499
Step 56; Avg weight = -10.237751
Particle 11; Weight = -10.113222
", - "Token: `53`
Context: SELECT age, zipcode FROM data WHERE vote < 50204051534494563504212179963090369236480764116603087141399318047989504651182745272122994422434899703499610
Step 57; Avg weight = -10.244302
Particle 11; Weight = -10.119304
", - "Token: `266`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212299442243489970349961053
Step 58; Avg weight = -10.249955
Particle 11; Weight = -10.128521
", - "Token: `24`
Context: SELECT age, zipcode FROM data WHERE vote < 5020405153449456350421217996309036923648076411660308714139931804798950465118274527212299442243489970349961053266
Step 59; Avg weight = -10.255388
Particle 11; Weight = -10.132102
", - "Token: `39`
Context: SELECT age, zipcode FROM data WHERE vote < 502040515344945635042121799630903692364807641166030871413993180479895046511827452721229944224348997034996105326624
Step 60; Avg weight = -10.262210
Particle 11; Weight = -10.137284
" + "Token: `,`
Context: SELECT vote
Step 3; Avg weight = -0.888091
Particle 11; Weight = -0.851550
", + "Token: ` zip`
Context: SELECT vote,
Step 4; Avg weight = -1.124863
Particle 11; Weight = -0.941903
", + "Token: `code`
Context: SELECT vote, zip
Step 5; Avg weight = -1.283818
Particle 11; Weight = -0.942447
", + "Token: ` FROM`
Context: SELECT vote, zipcode
Step 6; Avg weight = -1.382515
Particle 11; Weight = -1.031305
", + "Token: ` data`
Context: SELECT vote, zipcode FROM
Step 7; Avg weight = -1.593709
Particle 11; Weight = -1.193927
", + "Token: ` ORDER`
Context: SELECT vote, zipcode FROM data
Step 8; Avg weight = -1.701664
Particle 11; Weight = -1.311960
", + "Token: ` BY`
Context: SELECT vote, zipcode FROM data ORDER
Step 9; Avg weight = -1.831096
Particle 11; Weight = -1.312752
", + "Token: ` age`
Context: SELECT vote, zipcode FROM data ORDER BY
Step 10; Avg weight = -1.957496
Particle 11; Weight = -1.439440
", + "Token: ` ASC`
Context: SELECT vote, zipcode FROM data ORDER BY age
Step 11; Avg weight = -2.368902
Particle 11; Weight = -2.069171
", + "Token: ` ASC`
Context: SELECT age, zipcode, vote FROM data ORDER BY age
Step 13; Avg weight = -3.233764
Particle 11; Weight = -3.476535
", + "Token: ` <`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC
Step 14; Avg weight = -3.830131
Particle 11; Weight = -4.767722
", + "Token: `/`
Context: SELECT age, zipcode, vote FROM data ORDER BY age ASC <
Step 15; Avg weight = -5.379061
Particle 11; Weight = -11.204385
", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 6; Weight = -9.137509
↳ resampled as particle 11", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 7; Weight = -9.137509
↳ resampled as particle 11", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 8; Weight = -9.137509
↳ resampled as particle 11", + "Token: ` `
Context: SELECT vote, zipcode, age FROM data ORDER BY age ASC
Step 17; Avg weight = -7.068531
Particle 9; Weight = -9.137509
↳ resampled as particle 11", + "Token: `>`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC Step 17; Avg weight = -7.068531
Particle 10; Weight = -5.431045
↳ resampled as particle 11", + "Token: `>`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC Step 17; Avg weight = -7.068531
Particle 11; Weight = -5.431045
↳ resampled as particle 11", + "Token: `▪`
Context: SELECT age, gender FROM data WHERE gender>50 ORDER BY vote ASC
Step 19; Avg weight = -8.930951
Particle 11; Weight = -17.204494
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11, - 11, 11 ] }, @@ -4381,9799 +2083,925 @@ "color": "rgb(255.0, 0.0, 0.0)" }, "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 1, - 2 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 2, - 3 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 3, - 4 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 4, - 5 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 5, - 6 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 6, - 7 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 7, - 8 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 8, - 9 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 9, - 10 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 10, - 11 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 11, - 12 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 13, - 14 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 14, - 15 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 15, - 16 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 16, - 17 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 18, - 19 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 19, - 20 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 21, - 22 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - "type": "scatter", - "x": [ - 22, - 23 - ], - "y": [ - 0, - 0 - ] - }, - { - "line": { - "color": "rgb(255.0, 0.0, 0.0)" - }, - "mode": "lines", - "name": "0", - "opacity": 0.2, - "showlegend": false, - 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", + "image/png": 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", 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