Consider external factors such as weather or pitch conditions that might influence tactics.
<ol start="6" type[0]: # Copyright 2020 Google LLC
[1]: #
[2]: # Licensed under the Apache License, Version 2.0 (the "License");
[3]: # you may not use this file except in compliance with the License.
[4]: # You may obtain a copy of the License at
[5]: #
[6]: # http://www.apache.org/licenses/LICENSE-2.0
[7]: #
[8]: # Unless required by applicable law or agreed to in writing, software
[9]: # distributed under the License is distributed on an "AS IS" BASIS,
[10]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
[11]: # See the License for the specific language governing permissions and
[12]: # limitations under the License.
[13]: """A wrapper around tensorflow datasets."""
[14]: import functools
[15]: import numpy as np
[16]: import six
[17]: from tensorflow_datasets.core import api_utils
[18]: from tensorflow_datasets.core import dataset_utils
[19]: def _get_dataset_split(split_or_splits):
[20]: """Returns `tf.data.Dataset` object(s).
[21]: Args:
[22]: split_or_splits: Either:
[23]: – A `tfds.Split` instance.
[24]: – A list/tuple/set containing `tfds.Split` instances.
[25]: Returns:
[26]: If `split_or_splits` contains only one element it returns just that element,
[27]: otherwise it returns a dictionary mapping each split name (`str`) to its
[28]: corresponding `tf.data.Dataset`.
[29]: """
[30]: if isinstance(split_or_splits,
[31]: six.string_types + tuple(tfds.core.Splits.__args__)):
[32]: split_or_splits = [split_or_splits]
[33]: splits = {split.name: split.shard_info for split in split_or_splits}
[34]: builder = dataset_utils.get_builder_from_dataset_name(
[35]: list(splits.keys())[0])
***** Tag Data *****
ID: 1
description: The `_get_dataset_split` function handles different types of input arguments
(`tfds.Split` instance or collection thereof), converts them into dictionaries mapping
split names to shard information using TensorFlow Datasets utilities.
start line: 19
end line: 35
dependencies:
– type: Function
name: _get_dataset_split
start line: 19
end line: 35
context description: This function is central to handling different types of dataset-splitting,
leveraging TensorFlow Datasets utilities for managing splits efficiently.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: Y
*************
## Suggestions for complexity
1. **Custom Split Handling**: Allow users to pass custom-defined splits that aren't part of `tfds.Split`, including validation logic specific to those custom splits.
2. **Parallel Processing**: Implement parallel processing capabilities when dealing with large lists/tuples/sets of splits so that datasets can be loaded more efficiently.
3. **Dynamic Shard Allocation**: Introduce dynamic shard allocation based on real-time dataset statistics rather than static shard info.
4. **Split Transformation**: Enable transformations on each split before converting them into datasets – such as filtering certain data points based on user-defined criteria.
5. **Error Handling Enhancements**: Add comprehensive error handling mechanisms that log detailed information about failed operations related to dataset splitting.
## Conversation
: I need help understanding how [SNIPPET] works specifically regarding how it handles single vs multiple splits.
: Sure! In [SNIPPET], `_get_dataset_split` takes either a single `tfds.Split` instance or a collection containing multiple `tfds.Split` instances:
python
if isinstance(split_or_splits,
six.string_types + tuple(tfds.core.Splits.__args__)):
This checks if `split_or_splits` is either a string or an instance of any class within `tfds.core.Splits`. If it is just one element:
python
split_or_splits = [split_or_splits]
It wraps it into a list so it can be processed uniformly whether there’s one element or many.
Next:
python
splits = {split.name: split.shard_info for split in split_or_splits}
It creates a dictionary where each key is the name of a split (`split.name`) and each value is its shard info (`split.shard_info`). Finally:
python
builder = dataset_utils.get_builder_from_dataset_name(
list(splits.keys())[0])
It retrieves some kind of builder object related to this dataset name but doesn’t return anything yet because there seems to be missing code after this point.
Would you like me to expand this snippet further?
: Yes but I want it modified so if there’s only one split it applies some transformation function before returning it Can we make this transformation function parameterized?
: Absolutely! Here’s how you could modify your snippet so that if there’s only one split, it applies a transformation function provided by the user:
python
def _get_dataset_split(split_or_splits, transform_fn=None):
“””Returns `tf.data.Dataset` object(s).
Args:
split_or_splits : Either:
– A `tfds.Split` instance.
– A list/tuple/set containing `tfds.Split` instances.
transform_fn : Optional function applied if only one split exists.
Returns:
If `split_or_splits` contains only one element it returns just that element,
transformed by transform_fn if provided; otherwise it returns
a dictionary mapping each split name (`str`)
to its corresponding `tf.data.Dataset`.
“””
if isinstance(split_or_splits,
six.string_types + tuple(tfds.core.Splits.__args__)):
single_split = True
split_or_splits = [split_or_splits]
else:
single_split = False
splits = {split.name: split.shard_info for split in split_or_splits}
builder = dataset_utils.get_builder_from_dataset_name(
list(splits.keys())[0])
datasets_dict = {name : builder.as_dataset(shard_info=info)
for name, info in splits.items()}
if single_split:
first_key = next(iter(datasets_dict))
first_dataset = datasets_dict[first_key]
if transform_fn:
return transform_fn(first_dataset)
return first_dataset
return datasets_dict
Here’s what changed:
1. Added an optional parameter `transform_fn`.
2. Checked whether there was originally only one element before wrapping into a list using an additional flag (`single_split`).
3. Applied `transform_fn`, if provided when there’s only one dataset returned.
Let me know if you need further modifications!
: Great now I want this transformation function not just apply directly but also take additional parameters passed via kwargs How would we integrate kwargs here?
: Sure thing! We can modify `_get_dataset_split` so that any additional keyword arguments (`kwargs`) are passed along with the dataset when calling `transform_fn`. Here’s how you can do it:
python
def _get_dataset_split(split_or_splits, transform_fn=None, **kwargs):
“””Returns `tf.data.Dataset` object(s).
Args:
split_or_splits : Either:
– A tfds.Split instance.
– A list/tuple/set containing tfds.Split instances.
transform_fn : Optional function applied if only one split exists.
**kwargs : Additional keyword arguments passed along with transform_fn.
Returns:
If ‘split_or_splits’ contains only one element it returns just that element,
transformed by ‘transform_fn’ with ‘kwargs’ if provided;
otherwise returns a dictionary mapping each
split name (‘str’) to its corresponding ‘tf.data.Dataset’.
“””
if isinstance(split_or_splits,
six.string_types + tuple(tfds.core.Splits.__args__)):
single_split = True
split_or_splits = [split_or_splits]
else:
single_split = False
splits = {split.name: split.shard_info for split in split_or_splittings}
builder = dataset_utils.get_builder_from_dataset_name(
list(splits.keys())[0])
datasets_dict = {name : builder.as_dataset(shard_info=info)
for name , info in splits.items()}
if single_split :
first_key= next(iter(datasets_dict))
first_ds= datasets_dict[first_key]
if transform_fn :
return transform_fn(first_ds,**kwargs)
return first_ds
return datasets_dict
Here’s what changed:
1. Added an extra parameter capture mechanism via `**kwargs`.
2. When calling `transform_fn`, passed along these additional keyword arguments using `{**kwargs}` syntax.
Now your transformation function will receive any extra parameters specified when `_get_dataset_split` is called.
Is there anything else you’d like me to add?
*** Excerpt ***
The purpose of this study was threefold:. First,. Second,, demonstrating no statistically significant difference between groups.. Third,, indicating possible differences between groups.. These results support previous studies suggesting that MDTs have been effective at improving patient outcomes including reducing length-of-stay without increasing cost.[10–12] This study expands upon these findings by demonstrating similar results among patients treated at hospitals without MDTs prior year versus those treated at hospitals adopting MDTs during study year.[13] However,, suggesting improvement over time regardless of intervention.[14] The overall trend towards shorter length-of-stay suggests improvement over time independent of intervention.[15] It should be noted that our results show little evidence supporting increased cost associated with MDT implementation.. While our study did not demonstrate statistically significant differences between groups,. However,. Furthermore,. Therefore,. This study supports previous research indicating improved patient outcomes associated with MDT implementation.[16]
*** Revision 0 ***
## Plan
To create an exercise that challenges advanced comprehension skills while requiring profound understanding and additional factual knowledge beyond what’s presented directly within the excerpt itself requires several steps:
1. Incorporate technical terminology relevant not only within medical disciplines but also statistical analysis methodologies used within research studies focusing on multidisciplinary teams (MDTs).
2. Embed complex logical structures involving conditional statements (“if…then…”) alongside counterfactual reasoning (“even though…would have…”), making deductions less straightforward.
3. Introduce elements requiring outside knowledge about healthcare systems’ efficiency metrics beyond mere cost implications—such as patient satisfaction scores post-intervention or long-term health outcome improvements—which are not explicitly mentioned but are inherently linked with discussions about MDT effectiveness.
By rewriting the excerpt accordingly, we ensure that understanding requires both deep reading comprehension skills—navigating through dense academic language—and external knowledge about healthcare research methodologies and outcomes evaluation frameworks.
## Rewritten Excerpt
The objective delineated herein encompasses three principal facets concerning Multidisciplinary Teams’ efficacy within clinical settings vis-a-vis patient recuperation trajectories post-interventional deployment—a comparative analysis juxtaposing pre-and post-MDT integration periods across disparate hospital environments harboring variable baseline operational efficacies pre-interventional epoch versus those instituting MDT frameworks concomitantly within observational tenure demarcations delineated hereinbelow.
Primarily,—an exploration aimed at discerning statistical variances amidst cohorts subjected versus unexposed entities relative thereto interventions,—yielded null disparities signifying homogeneity across analyzed spectra vis-a-vis primary outcome metrics encompassing recuperative temporal spans sans augmentative fiscal exigencies engendered therewithin interventions—a corroboration resonant with antecedent scholarly expositions positing analogous efficacious paradigms devoid of economic burden exacerbations consequent upon MDT integrations therein referenced numerically thereinbelow sequentially.
Subsequently,—an evaluative discourse underscoring potential divergences amidst analyzed collectives—evinced through inferential statistical methodologies revealing nuanced discrepancies potentially indicative of underlying heterogeneities necessitating further investigational scrutiny beyond superficial analytical perusals thereby suggesting latent dimensions warranting deeper exploratory endeavors henceforth elucidated thereinbelow sequentially delineated numerical references thereto scholarly discourses aforementioned thereinbelow respectively,
Tertiarily,—the overarching thematic trajectory insinuates incremental amelioration across temporal spectrums irrespective adjunctive interventionist modalities—a testament underscoring intrinsic systemic evolutions transcending immediate interventional impacts thereby implicating broader systemic enhancements potentially contributory towards observed phenomena notwithstanding explicit interventionist implementations thereof henceforth discussed hereinbelow sequentially referenced numerically thereto scholarly discourses antecedently cited thereinbelow respectively,
Conclusively,—while empirical evidence substantiating pronounced differential impacts attributable directly unto MDT implementations remains elusive per se—indirectly inferred implications suggest incremental systemic enhancements potentially attributable unto broader institutional adaptations concomitant upon MDT integration efforts thereby corroborating antecedent academic conjectures positing improved patient-centric outcomes resultant upon said integrations therein referenced numerically thereto scholarly expositions antecedently cited hereinbelow sequentially.
## Suggested Exercise
Given the complex nature of evaluating Multidisciplinary Teams’ impact within healthcare settings as outlined above—which integrates comparative analyses pre-and post-MDT implementation across varying hospital efficiencies—considering both direct outcome measures such as length-of-stay reductions without increased costs alongside inferred systemic improvements over time independent of direct interventions:
Which statement best encapsulates an inferred conclusion derived from analyzing longitudinal data sets comparing hospitals adopting Multidisciplinary Teams during versus prior years?
A) Hospitals implementing Multidisciplinary Teams demonstrated significantly shorter patient stays compared to those not adopting such models due solely directly attributable cost increases associated with MDT integration efforts.
B) Despite lacking direct statistical significance across primary outcome measures between hospitals adopting Multidisciplinary Teams versus those not doing so during respective observational periods, indirect evidence suggests systemic improvements likely contributed by broader institutional adaptations concurrent with MDT integration efforts may enhance patient outcomes indirectly over time independent of immediate intervention effects.
C) Statistical analyses conclusively demonstrate Multidisciplinary Teams directly cause improved long-term health outcomes across all measured metrics compared against traditional care models without consideration toward potential indirect systemic benefits arising from broader institutional changes accompanying MDT implementations.
D) The sole factor contributing towards observed improvements over time within hospital settings regarding patient recovery trajectories post-Multidisciplinary Team implementation lies exclusively within enhanced economic efficiencies achieved through reduced operational costs inherent solely unto direct intervention effects thereof.
*** Revision 1 ***
check requirements:
– req_no: 1
discussion: The draft does not require advanced knowledge external to itself; all
choices relate directly back without needing external facts.
score: 0
– req_no: 2
discussion: Understanding subtleties seems necessary but could be clearer; choices
could better reflect nuances mentioned.
score: 2
– req_no: 3
discussion: Excerpt length meets requirement but clarity could improve readability.
score: 3
– req_no: 4
discussion: Choices are misleading but could benefit from tighter alignment with
external knowledge requirement.
score: 1
– req_no: 5
question_difficulty_met? Y/N? N Yes No clarification needed why No would suffice?
– req_no’: The exercise lacks sufficient difficulty given no necessity for external,
advanced undergraduate knowledge application beyond basic comprehension skills;
question_discclarity_and_complexity_met? Y/N? N Yes No clarification needed why No would suffice?
external fact_needed? Y/N? N Yes No clarification needed why No would suffice?
revision_suggestion”: To meet requirement number one effectively incorporating advanced,
external knowledge necessitates intertwining specific theoretical frameworks or empirical findings outside those immediately presented within your excerpt itself.nnFor example:nnYou could connect concepts discussed about Multidisciplinary Teams’ efficacy directly back towards established theories concerning organizational behavior change theories like Kotteru2019s eight-step process u2018Leading Changeu2019.nnThen pose questions relating choices back towards whether evidence presented supports assumptions made by such theories concerning incremental change versus radical change.nnAdditionally:nnEnsure your choices require distinguishing subtle differences drawn out through nuanced reading comprehension tied closely together with understanding these theories.nnEnhance complexity through referencing empirical studies which might support assertions made subtly throughout your text.”
revised excerpt”: “The objective delineated herein encompasses three principal facets concerning Multidisciplinary Teams’ efficacy within clinical settings vis-a-vis patient recuperation trajectories post-interventional deployment—a comparative analysis juxtaposing pre-and post-Multidisciplinary Team integration periods across disparate hospital environments harboring variable baseline operational efficacies pre-interventional epoch versus those instituting Multidisciplinary Team frameworks concomitantly within observational tenure demarcations delineated hereinbelow.nPrimarily—an exploration aimed at discerning statistical variances amidst cohorts subjected versus unexposed entities relative thereto interventions—yielded null disparities signifying homogeneity across analyzed spectra vis-a-vis primary outcome metrics encompassing recuperative temporal spans sans augmentative fiscal exigencies engendered therewithin interventions—a corroboration resonant with antecedent scholarly expositions positing analogous efficacious paradigms devoid of economic burden exacerbations consequent upon Multidisciplinary Team integrations therein referenced numerically thereinbelow sequentially.nSubsequently—an evaluative discourse underscoring potential divergences amidst analyzed collectives—evinced through inferential statistical methodologies revealing nuanced discrepancies potentially indicative of underlying heterogeneities necessitating further investigational scrutiny beyond superficial analytical perusals thereby suggesting latent dimensions warranting deeper exploratory endeavors henceforth elucidated thereinbelow sequentially delineated numerical references thereto scholarly discourses aforementioned thereinbelow respectively,nTertiarily—the overarching thematic trajectory insinuates incremental amelioration across temporal spectrums irrespective adjunctive interventionist modalities—a testament underscoring intrinsic systemic evolutions transcending immediate interventional impacts thereby implicating broader systemic enhancements potentially contributory towards observed phenomena notwithstanding explicit interventionist implementations thereof henceforth discussed hereinbelow sequentially referenced numerically thereto scholarly discourses antecedently cited thereinbelow respectively,nConclusively—while empirical evidence substantiating pronounced differential impacts attributable directly unto Multidisciplinary Team implementations remains elusive per se—indirectly inferred implications suggest incremental systemic enhancements potentially attributable unto broader institutional adaptations concomitant upon Multidisciplinary Team integration efforts thereby corroborating antecedent academic conjectures positing improved patient-centric outcomes resultant upon said integrations therein referenced numerically thereto scholarly expositions antecedently cited hereinbelow sequentially.”
correct choice”: Despite lacking direct statistical significance across primary outcome measures between hospitals adopting Multidisciplinary Teams versus those not doing so during respective observational periods, indirect evidence suggests systemic improvements likely contributed by broader institutional adaptations concurrent with MDT integration efforts may enhance patient outcomes indirectly over time independent of immediate intervention effects.”
revised exercise”: |-
Given your understanding from analyzing longitudinal data sets comparing hospitals adopting Multidisciplinary Teams during various epochs—as discussed above—and considering established organizational behavior change theories such as Kotteru2019s eight-step process u2018Leading Changeu2019,
Which statement best encapsulates an inferred conclusion derived from analyzing longitudinal data sets comparing hospitals adopting Multidisciplinary Teams during versus prior years?
incorrect choices”:
– Hospitals implementing Multidisciplinary Teams demonstrated significantly shorter patient stays compared solely due directly attributable cost increases associated with MDT integration efforts.”
– Statistical analyses conclusively demonstrate Multidisciplinary Teams directly cause improved long-term health outcomes across all measured metrics compared against traditional care models without consideration toward potential indirect systemic benefits arising from broader institutional changes accompanying MDT implementations.”
– The sole factor contributing towards observed improvements over time within hospital settings regarding patient recovery trajectories post-Multidisciplinary Team implementation lies exclusively within enhanced economic efficiencies achieved through reduced operational costs inherent solely unto direct intervention effects thereof.”
<>I’m trying wrap my head around some Python code here involving threading locks while working on tensor manipulations using PyTorch tensors stored inside nested dictionaries representing batches and labels which are then manipulated based on certain indices provided externally perhaps coming from another system component I’m thinking maybe something like TensorRT given some context clues although I’m not entirely sure Could you help clarify what exactly happens step-by-step especially around acquiring releasing locks modifying tensors creating new ones deleting entries etc basically I’m curious about how all these pieces fit together thanks
