Redlands United U23: A Comprehensive Guide for Sports Bettors

Overview of Redlands United U23

Redlands United U23, a prominent football team based in the vibrant region of Redlands, competes in the National Youth League. The team, founded in 2005, is currently coached by renowned manager Alex Thompson. Known for their dynamic play and strategic acumen, they adopt a 4-3-3 formation that emphasizes attacking prowess.

Team History and Achievements

Since its inception, Redlands United U23 has established itself as a formidable force in youth football. They have clinched multiple league titles and have been regular contenders for the top spots. Notable achievements include winning the National Youth League three times and securing several domestic cups. Their most memorable season was in 2018 when they finished as league runners-up.

Current Squad and Key Players

The squad boasts several standout players who have been instrumental in their recent successes. Key players include:

• Liam Carter – Forward (Goals: 12)
• Marcus Lee – Midfielder (Assists: 10)
• Jake Thompson – Defender (Clean Sheets: 8)

Team Playing Style and Tactics

The team primarily plays a high-pressing game with a focus on quick transitions from defense to attack. Their 4-3-3 formation allows them to dominate possession while maintaining defensive solidity. Strengths include their fast-paced attacks and disciplined defense, while weaknesses lie in occasional lapses in concentration leading to counterattacks.

Interesting Facts and Unique Traits

Fans affectionately call the team “The Red Blades” due to their sharp attacking style. The team has a passionate fanbase known for their vibrant support at home games. Rivalries with teams like Greenfield FC add an extra layer of excitement to their matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• Liam Carter: Top scorer ✅
• Marcus Lee: Assist leader 🎰
• Jake Thompson: Defensive stalwart 💡

Comparisons with Other Teams in the League or Division

In comparison to other teams in the division, Redlands United U23 stands out for their balanced approach between attack and defense. While teams like Blue City FC focus heavily on offensive play, Redlands maintains a more measured strategy.

Case Studies or Notable Matches

A breakthrough game was against Riverdale United last season, where they secured a stunning 4-1 victory that propelled them into playoff contention. This match showcased their tactical flexibility and resilience under pressure.

Stat Category	Last Season Performance	This Season Projection
Total Goals Scored	45	N/A
Total Clean Sheets	12	N/A
Average Possession (%)	58%	N/A

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡

• Analyze head-to-head records against upcoming opponents for better betting decisions.

• Maintain awareness of player injuries which could impact team performance.

• Favor bets on home games where they tend to perform stronger due to crowd support.

Famous Quotes about Redlands United U23 (Quote Block)

“Redlands United U23 is not just a team; it’s a testament to strategic brilliance.” – Sports Analyst John Smith.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists ✅❌ Lists ✅❌ Lists ✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists✅❌ Lists

• Pros:

• Dominant midfield control ✅🎰💡

• Possessive attacking strategies 🎰💡🔥

 

• Cons:</lcagriozel/autogluon/autogluon/text/text_prediction/predictor.py
  # Copyright (c) Meta Platforms, Inc. and affiliates.
  #
  # Licensed under the Apache License, Version 2.0 (the “License”);
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  # http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an “AS IS” BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.

  import logging

  from typing import Dict

  from autogluon.core.constants import BINARY_TASKS

  logger = logging.getLogger(__name__)

  class TextPredictor:
  def __init__(self):
  self.model = None
  self.task_type = None

  def fit(
  self,
  train_data,
  time_limit=None,
  presets=None,
  hyperparameters=None,
  **kwargs):
  “””Train models on training data.

  Parameters
  ———-
  train_data : str or pandas.DataFrame
  Path(s) to training data file(s), or DataFrame containing training data.
  If str is given, each line must be formatted as `labelttext`.
  If pandas.DataFrame is given instead of str path(s),
  it must contain columns named ‘label’ and ‘text’.
  If label column contains multiple labels per example separated by spaces,
  multi-label classification will be performed.
  For regression tasks specify target column name as ‘target’.

  time_limit : int
  Time limit (in seconds) for fitting model.

  presets : str
  Preset configuration name used during model search.

  hyperparameters : dict(str -> dict)
  Dictionary mapping model names (str) to hyperparameter search space dictionaries (dict).

  kwargs : dict(str -> object)
  Additional arguments passed through to underlying model training algorithms.
  “””
  raise NotImplementedError(“fit method needs implementation.”)

  def load_model(self):
  “””Load pre-trained model.”””
  raise NotImplementedError(“load_model method needs implementation.”)

  def predict(self,
  texts,
  output_mode=’binary’,
  **kwargs) -> Dict[str, float]:
  “””Perform inference using trained models.

  Parameters
  ———-
  texts : list[str] or str
  List of strings representing text samples on which inference should be performed.

  output_mode : {‘binary’, ‘multiclass’, ‘regression’}
  Output mode specifying task type.
  In binary mode only two classes are considered (‘True’/’False’).
  In multiclass mode all classes are considered (‘True’/’False’/’Other’).
  In regression mode numerical values are predicted.

  kwargs : dict(str -> object)
  Additional arguments passed through to underlying prediction algorithms.

  Returns
  ——-
  predictions : dict(str -> float)
  Dictionary mapping text samples given as input (`texts`) keys to prediction scores (`float`) values.
  “””

  if isinstance(texts,str):
  texts=[texts]

  if self.task_type == ‘regression’:
  return {text: float(prediction[0]) for text,prediction in zip(texts,predictions)}
  elif self.task_type == BINARY_TASKS[‘multiclass’]:
  return {text: prediction[0] if prediction[0] > prediction[1] else prediction[1]
  if prediction[1] > prediction[0] else ‘Other’
  for text,prediction in zip(texts,predictions)}
  elif self.task_type == BINARY_TASKS[‘binary’]:
  return {text: prediction[0] if prediction[0] > prediction[1] else prediction[1]
  for text,prediction in zip(texts,predictions)}
  else:
  raise ValueError(‘Unexpected task type.’)
  else:
  return {text: float(prediction) for text,prediction in zip(texts,predictions)}

  <|file_sep
  from .predictor import TextPredictorcagriozel/autogluon<|file_sep#!/usr/bin/env python3

  import argparse

  def main():
  parser = argparse.ArgumentParser()
  parser.add_argument('–input_dir', type=str)
  parser.add_argument('–output_dir', type=str)
  args = parser.parse_args()

  paths = []
  with open(args.input_dir + '/files.txt') as f:
  for line in f.readlines():
  line = line.strip()
  paths.append(line)

  with open(args.output_dir + '/files.txt','w') as f:
  for path in paths:
  f.write(path+'n')

  if __name__ == '__main__':
  main()<|file_sep`autogluon.text` contains utilities related to natural language processing.cagriozel/autogluon<|file_sep ado –no-save clear mata mata set matastrict off clear all set more off set matsize 10000 global numThreads=8 set obs $numThreads global seed=1234 //set seed(1234)

  * Sample size per group is fixed at n=10000; number of groups varies from N=10k up to N=100k*
  global N=10000 //number of groups; i.e., number of unique values taken by variable group_id*
  global n=10000 //sample size per group*

  //Generate group ids that are uniformly distributed over [N]
  gen id=_n+floor(_n/$N)*$N

  //Randomly sample n observations from each group_id using Mata function `uniqsample`
  mata:

  void uniqsample(st_matrix Y,st_matrix X,st_matrix W,int nobs,int nvars,out st_matrix res_Y,out st_matrix res_X,out st_matrix res_W){

  real matrix Y=X,W;
  real matrix randY=Y;

  int rows=Y[,1],cols=Y[1,.],grps=nobs/nvars;

  Y=randsamp(Y,n);
  W=randsamp(W,n);
  X=randsamp(X,n);

  res_Y=Y;
  res_X=X;
  res_W=W;
  }

  end

  clear mata

  mata:

  //Group id matrix
  st_view(id=.,.,`"`c(current_var)'")

  //Matrix containing outcome variable y
  st_view(y=.,.,"`y'")
  st_view(y_,.,"`y'")

  //Matrix containing treatment variables x
  st_view(x=.,.,"`x'")
  st_view(x_,.,"`x'")

  //Matrix containing confounders w
  st_view(w=.,.,"`w'")
  st_view(w_,.,"`w'")

  uniqsample(y,x,w,$n,$N,y_,x_,w_)
  end

  clear mata

  drop _all
  set obs $numThreads

  gen id=_n+floor(_n/$numThreads)*$numThreads

  gen y=y_
  replace y=. if id!=id[_n-1]
  gen x=x_
  replace x=. if id!=id[_n-1]
  gen w=w_
  replace w=. if id!=id[_n-1]

  egen grp_id=count(id),by(id)

  bysort grp_id: egen mean_y=mean(y)
  bysort grp_id: egen mean_x=rowtotal(x*)
  bysort grp_id: egen mean_w=rowtotal(w*)

  save "${tempdir}/data.dta", replace cagriozel/autogluon<|file_sep[image]: # AutoGluon Image Prediction API Reference

  ## `ImagePredictor`

  ### `fit()`

  python
  def fit(
  self,
  data_path_or_dataframe_train=None,
  label_column='label',
  time_limit=None,
  preprocess=False,
  num_trials=None,
  num_early_stops=None,
  hyperparameters=None,
  fine_tune=False,
  exclude_nas=False,
  metric=None,
  output_directory=None):

  #### Parameters:

  * `data_path_or_dataframe_train`: Data path(s) pointing towards image files used during training phase.
  If multiple paths are provided then they should be comma-separated strings within one single string argument such that each path points towards one directory containing images belonging to one single class label.
  Alternatively you can provide pandas DataFrame directly instead of paths pointing towards image files.
  In this case you need also provide label column name (`label_column`) which contains class labels corresponding each image file path stored within your DataFrame.

  * `label_column`: Label column name used within provided DataFrame during training phase when providing DataFrame directly instead of paths pointing towards image files.

  * `time_limit`: Time limit specified during model search process.

  * `preprocess`: Boolean flag indicating whether preprocessing should be performed before model search process.

  * `num_trials`: Number of trials specified during model search process.

  * `num_early_stops`: Number early stopping rounds specified during model search process.

  * `hyperparameters`: Hyperparameter dictionary used during model search process.

  * `fine_tune`: Boolean flag indicating whether fine-tuning should be performed after initial AutoGluon Image Predictor initialization.

  * `exclude_nas`: Boolean flag indicating whether models performing worse than random guessing should be excluded from ensembling process.

  * `metric`: Metric used during model selection process among candidate models generated throughout AutoGluon Image Predictor initialization phase.

  * `output_directory`: Directory where trained models will be saved after completion.

  ### `load_model()`

  python
  def load_model(
  self):

  #### Returns:

  Trained AutoGluon Image Predictor instance.

  ### `predict()`

  python
  def predict(
  self,data_paths,output_mode='multiclass'):

  #### Parameters:

  `data_paths`: Paths pointing towards image files upon which inference will be performed using previously trained AutoGluon Image Predictor instance.

  `output_mode`: Output mode specifying task type (`{'binary','multiclass','regression'}`).

  #### Returns:

  Dictionary mapping each image file path (`str`) provided as input (`data_paths`) key into corresponding predicted class label (`str`) value.<|file_sep–! /usr/bin/env python3
  –! -*- coding:utf-8 -*-

  import argparse
  import os
  import numpy as np
  import random

  parser = argparse.ArgumentParser()

  parser.add_argument("–input_dir", type=str)
  parser.add_argument("–output_dir", type=str)

  args = parser.parse_args()

  paths=[]
  with open(args.input_dir+'/files.txt') as f:
  for line in f.readlines():
  line=line.strip()
  paths.append(line)

  random.shuffle(paths)

  with open(args.output_dir+'/files.txt','w') as f:
  for path in paths:
  f.write(path+'n')

  for ptype,pathlist_filetype_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_tuple_list_
  tuple_
  in enumerate(zip(paths,[["jpg","png"]]*len(paths))):
  path,pathlist_filetype_list=pathlist_filetype_list_tuple_0,pathlist_filetype_list_tuple_1

  pathlist=path.split('/')
  filepath=pathlist[-1]

  if filepath.endswith(".zip"):

  filepath=os.path.join("/".join(pathlist[:-1]),filepath[:-4])

  elif filepath.endswith(".tar.gz"):

  filepath=os.path.join("/".join(pathlist[:-1]),filepath[:-7])

  path=os.path.join(filepath,"*.{}".format(random.choice(pathlist_filetype_list)))

  path=os.path.join(args.output_dir,str(ptype)+"_"+os.path.basename(path))

  os.makedirs(os.path.dirname(path),exist_ok=True)

  os.system("ln -sf {} {}".format(os.path.abspath(os.readlink(path)),path))

  if not os.path.exists(os.path.dirname(path)):
  os.makedirs(os.path.dirname(path))

  os.symlink(os.readlink(file),"{}".format(os.path.abspath(path)))

  cagriozel/autogluon<|file_sepHttp Request Format Specification:

  POST /v1/predict HTTP/1.1
  Host: localhost
  Content-Type: application/json
  Accept-Encoding: gzip

  {
  "model": "”,
  “task”: “”,
  “input”: “”
  }

  HTTP Response Format Specification:

  HTTP/1.x xxx OK
  Content-Type: application/json

  {
  “predictions”: []
  }

  Python Client Example Usage:

  import requests,json

  requests.post(url=’http://localhost/v1/predict’,headers={‘Content-Type’:’application/json’},json={
  “model”:”my-model-name”,
  “task”:”classification”,
  “input”:”my-input”
  }).json()

  returns

  {
  “predictions”:[
  {“prediction”:”my-prediction”}
  ]
  }

  REST API Endpoint Specifications:

  Endpoint Name | Supported HTTP Methods | Description | Required Input Parameters | Optional Input Parameters | Return Values | Expected Response Code
  — | — | — | — | — | — | —
  /models/list_models | GET | List all registered models within registry directory specified by “registry_directory“ environment variable.| None.| None.| List containing registered models within registry directory specified by “registry_directory“ environment variable.| “200 OK“
  /models/add_model/| POST | Add new model identified by “model-name“ into registry directory specified by “registry_directory“ environment variable.| “model-name“.| None.| Model entry added into registry directory specified by “registry_directory“ environment variable.| “200 OK“
  /models/delete_model/| DELETE | Delete existing registered model identified by “model-name“ from registry directory specified by “registry_directory“ environment variable.| “model-name“.| None.| Model entry deleted from registry directory specified by “registry_directory“ environment variable.| “200 OK“
  /models/update_model/| PUT | Update existing registered model identified by “model-name“ within registry directory specified by “registry_directory“ environment variable.| None.| None.| Model entry updated within registry directory specified by “registry_directory“ environment variable.| “200 OK“
  /models/get_model/| GET | Retrieve existing registered model identified by “model-name“, including associated metadata such as creation date/time etc..within registry directory specified by “registry_directory“ environment variable.| None.| None.| Model entry retrieved from registry directory specified by “registry_directory“ environment variable along with associated metadata such as creation date/time etc..within it’s JSON representation format..|
  200 OK“
  /models/predict//| POST | Perform inference using previously trained registered machine learning pipeline identified using both it’s unique identifier name represented via parameter named “ along with task-type represented via parameter named “ upon serialized input data represented via parameter named “ passed through JSON request body payload encoded using UTF-8 character encoding format specification..If any error occurs during inference then appropriate error message will also be returned back along side HTTP response code representing corresponding status code value indicating failure condition occurred due some reason..In case successfull execution then list containing predicted results computed based upon given inputs shall always returned back regardless whether any exception raised otherwise..This particular endpoint requires both authentication mechanism enabled through basic authentication scheme supported out-of-the-box utilizing username/password credentials provided directly via command-line interface arguments supplied when invoking RESTful API server executable binary script file located somewhere inside system PATH environment variables defined globally across entire operating system being used currently running currently active session instance currently being executed currently ongoing right now exactly at this very moment precisely at this exact point right here immediately without any delay whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoever absolutely no exceptions whatsoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever whatsoeversoever .
  Required Input Parameters:
      **Input Data**: Serialized input data upon which inference will be performed.
  Optional Input Parameters:
      None.
  Return Values:
      List containing predicted results computed based upon given inputs.
  Expected Response Code:
      **200 OK**: Successfully performed inference using previously trained registered machine learning pipeline identified using both it’s unique identifier name represented via parameter named “ along with task-type represented via parameter named “ upon serialized input data represented via parameter named “ passed through JSON request body payload encoded using UTF-8 character encoding format specification..cagriozel/autogluon<|file_sep shell script

  ./train.sh –config ./configs/config.yaml –dataset ./datasets –out ./models

  config.yaml

  yaml

  train_dataset_path=./datasets/train.csv

  valid_dataset_path=./datasets/valid.csv

  test_dataset_path=./datasets/test.csv

  eval_metrics=["accuracy"]

  preprocess_config={
  "image_size": [224],
  "image_channels": [3],
  "label_encoder": {
  "class_names":["a","b","c"],
  "class_weights":[10,-10],
  "allow_incomplete_classes":[true,false]
  },
  "text_preprocessing":{
  "tokenizer":{
  "type":"bert",
  "lowercase": true},
  "text_augmentation":{
  "type":"back_translation",
  "languages":["en","fr"],
  "use_random_seed": true}
  }
  }

  hyperparameters={
  "sampling_method":"oversample",
  "ensemble_method":"weight_sum"
  }

  search_space={
  "sampling_method":["oversample","undersample"],
  "ensemble_method":["weight_sum","stacking"]
  }

  time_limit_per_trial=3600 #in seconds

  search_strategy="bayesian"
  early_stopping_rounds=20 #number early stopping rounds

  presets=["best_quality","fast_inference"]

  epochs_per_trial=[5]
  batch_sizes_per_trial=[64]

  fine_tune_epochs=[5]
  fine_tune_batch_sizes=[64]

  exclude_nas=true

  metrics=["accuracy"]

  framework="tf"

  architecture="resnet"

  loss_function="cross_entropy"

  optimizer="adam"

  learning_rate_schedules=[
  {
  "name":"exponential_decay",
  "inital_learning_rate":0.001,"decay_steps":100000,"decay_rate":0.96,"staircase":true},
  {
  "name":"polynomial_decay",
  "inital_learning_rate":0.001,"decay_steps":100000,"end_learning_rate":0.00001,"power":0.5},
  {
  "name":"piecewise_constant",
  "inital_learning_rate":0.001,"boundaries":[50000],"values":[0.001,.0005]},
  {
  "name":"natural_exp_decay",
  "inital_learning_rate":0.001,"halflife_seconds":{"cpu_time":-99},"steps_per_execution":{"cpu_time":-99}},
  {
  "name":"cosine_decay_restarts",
  "inital_learning_rate":0,.001,"first_decay_steps":-99,"t_mul":-99,"m_mul":-99}
  ]

  learning_rates=[
  {"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""}
  ]

  momentum_values=[
  {"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""},{"name":"","value":""}]

  dropout_rates=[
  {"layer_index":-99,"rate":-99},
  {"layer_index":-99,"rate":-99},
  {"layer_index":-99,"rate":-99},
  {"layer_index":-99,"rate":-99},
  {"layer_index":-99,"rate":-99}]

  lrschedule_values=[
  {"schedule_index":[-9,-9],"schedule_value":[-9,-9]},
  {"schedule_index":[-9,-9],"schedule_value":[-9,-9]},
  {"schedule_index":[-9,-9],"schedule_value":[-9,-9]},
  {"schedule_index":[-9,-9],"schedule_value":[-9,-9]},
  {"schedule_index":[-9,-9],"schedule_value":[-9,-9]}]

  momentum_values=
  [
  {"index":[-999999999999999999999999999999999999999],"momentum_value":[-999999999999]}
  ,{"index":[-88888888888888888888888888888888],"momentum_value":[-88]}
  ,{"index":[-7777777777777777777777],"momentum_value":[-77]}
  ,{"index":[-66666666666666],"momentum_value":[-66]}
  ,{"index":[-55555555555],"momentum_value":[-55]}]

  dropout_rates=
  [
  {"layer_index":[11111111111],"rate":[11]},
  {"layer_index":[22222222222],"rate":[22]},
  {"layer_index":[33333333333],"rate":[33]},
  {"layer_index":[44444444444],"rate":[44]},
  {"layer_index":[55555555555],"rate":[55]}]

  lrschedule_values=
  [
  {"schedule_indices":[{"index_one":[{"training_step_one":{"cpu_time_one":{"seconds_one",-11},"wallclock_time_one":{"seconds_one",-11}}},"index_two":[{"training_step_two":{"cpu_time_two":{"seconds_two",-22},"wallclock_time_two":{"seconds_two",-22}}}]},
  "indices_three":[{"training_step_three":{"cpu_time_three":{"seconds_three",-33},"wallclock_time_three":{"seconds_three",-33}}}]},
  "indices_four":[{"training_step_four":{"cpu_time_four":{"seconds_four",-44},"wallclock_time_four":{"seconds_four",-44}}}]],
  "indices_five":[{"training_step_five":{"cpu_time_five":{"seconds_five",-55},"wallclock_time_five":{"seconds_five",-55}}}]}],
  "schedules":[{"one_schedule_one":[{"learning_rate_one_one",{"float":11111111111}}],
  "two_schedules_two":[{"learning_rate_two_two",{"float":22222222222}}],
  "three_schedules_three":[{"learning_rate_three_three",{"float":33333333333}}],
  "four_schedules_four":[{"learning_rate_four_four",{"float":44444444444}}],
  "fiveschedules_five_fives":[{"learning_rate_five_five",{"float":55555555555}}]}]}
  ,{"schedule_indices":[{"index_one":[{"training_step_one":{"cpu_time_one":{"seconds_one",-111}},
  "index_two":[{"training_step_two":{"cpu_time_two":{"seconds_two",-222}}}},{indices_three:[{training_step_three:{cpu_time_three:{seconds_three:-333}}}},{indices_four:[{training_step_four:{cpu_time_four:{seconds_four:-444}}}]],indices_five:[{training_step_five:{cpu_time_five:{seconds_five:-555}}}]}]},
  "schedules":[{"one_schedule_one":[{
  learning_rate_one_one,{"float":11111111111}}],two_schedules_two":[{
  learning_rate_two_two,{"float":22222222222}}],three_schedules_three":[{
  learning_rate_three_three,{"float":33333333333}}],four_schedules_four":[{
  learning_rate_four_four,{"float":44444444444}}],fiveschedules_five_fives":[{
  learning_rate_five_five,{"float":55555555555}}]}]}
  ,{"schedule_indices":
  [{index_one:[{training_step_one:{cpu_time_one:{seconds_one:-111}}, index_two:[{training_step_two:{cpu_time_two:{seconds_two:-222}}}],[indices_three:[{training_step_three:{cpu_time_three:{seconds_thirteen:-333}}}],[indices_four:[{training_step_fourt:{cpu_timetwo{-444}}}],[indices_fives:[{traingstepfive:{cputimefive{-555}}}]]}], schedules:[
  {{one_schedule_on:[
  learaningratheoneone,{floar}:11111111111}], two_scheduless_tw:[
  learaningrathe_twotwo,{floar}:22222222222}], three_scheduless_thre:[
  learaningrathe_threethree,{floar}:33333333333}], four_scheduless_for:[
  learaningrathe_forfour,{floar}:44444444444}], five_scheduless_fiv[
  learaningrathe_fivfive,{floar}:55555555555}]},{schdull_indies:[
  {{indix_ein[{trainigstepp_einrtey-{cputimme_einrtey-secons_eintey{-12}}],
  indix_etwo[{trainigstepp_etwo-{cputimme_etwo-secons_etwo{-22}}},
  indix_ethree[{trainigstepp_ethree-{cputimme_ethree-secons_ethree{-33}}},
  indix_efour[{trainigstepp_efour-{cputimme_efour-secons_efour{-44}}},
  indix_efive[{trainigstepp_efive-{cputimme_efive-secons_efive{-55}}}]]}],
  schedulls[[{{schedull_onene[{learnningrattne_onneone,{flaf}{11212121212121}}},
  schedull_twotwoo[{learnningrattne_twotwoo,{flaf}{22323232323232}}],
  schedull_threethee[{learnningrattne_threethee,{flaf}{33434343434343}}],
  schedull_forfour[{learnningrattne_forfour,{flaf}{44545454545454}}],
  schedull_fivfive[{learnningrattne_fivfive,{flaf}{55656565656565}}}]]}]]
  }

  ]

  early_stopping_patience=-10 #number epochs without improvement after which trial will terminate automatically even though time limit hasn't expired yet

  max_trials=-30 #maximum number trials allowed before terminating entire AutoGluon Image Predictor initialization phase prematurely regardless whether time limit has expired yet

  min_resources=-40 #minimum amount resources allocated per trial before terminating entire AutoGluon Image Predictor initialization phase prematurely regardless whether time limit has expired yet

  max_resources=-50 #maximum amount resources allocated per trial before terminating entire AutoGluon Image Predictor initialization phase prematurely regardless whether time limit has expired yet

  num_cpus=-60 #number CPU cores available locally

  gpus=-70 #number GPUs available locally

  memory_gb=-80 #amount memory available locally measured unit gigabytes GB

  disk_gb=-90 #amount disk space available locally measured unit gigabytes GB

  random_state=-91

  seed=-92

  Dataset Format Specification:

  CSV file consisting following columns:

  image_paths,label,image_height,image_width,image_channels,label_encoder_encoded_label,text_inputs,text_preprocessed_inputs,text_augmented_inputs,class_weights,class_names,is_incomplete_class,label_encoder_class_names

  Example Dataset Sample:

  /home/user/images/a/image_00001.jpg,a,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,true,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o

  /home/user/images/b/image_00002.jpg,b,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,b,c,d,e,f,g,h,i,j,k,l,m,n,o,false,b,c,d,e,f,g,h,i,j,k,l,m,n,o

  /home/user/images/c/image_00003.jpg,c,a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,c,d,e,f,g,h,i,j,k,l,m,n,o,true,c,d,e,f,g,h,i,j,k,l,m,n,o

  Model Format Specification:

  Directory consisting following sub-directories:

  config/model_config.json config/preprocess_config.json config/search_space.json config/hyperparameters.json config/pretrained_models.pth.tar config/trained_models.pth.tar

  Evaluation Metric Format Specification:

  JSON file consisting following keys:

  accuracy precision recall auc_roc auc_pr fpr tpr precision_curve recall_curve roc_curve pr_curve

  Framework Format Specification:

  Framework-specific binary executable script file saved under current working directory.

  Architecture Format Specification:

  Architecture-specific binary executable script file saved under current working directory.

  Loss Function Format Specification:

  Loss function-specific binary executable script file saved under current working directory.

  Optimizer Format Specification:

  Optimizer-specific binary executable script file saved under current working directory.

  Learning Rate Schedule Format Specification:

  Learning rate schedule-specific binary executable script file saved under current working directory.

  Momentum Value Format Specification:

  Momentum value-specific binary executable script file saved under current working directory.

  Dropout Rate Format Specification:

  Dropout rate-specific binary executable script file saved under current working directoy.

  LRSchedule Value Format Specification:

  LRSchedule value-specific binary executable script file saved under current working directoy.

  Training Script File Example Usage:

  Usage:

  python train.py –config=config.yaml –dataset=

  Example Command Line Invocation:

  python train.py –config=config.yaml –dataset=./datasets

  Testing Script File Example Usage:

  Usage:

  python test.py –config=config.yaml –dataset=

  Example Command Line Invocation:

  python test.py –config=config.yaml –dataset=./datasets

  Prediction Script File Example Usage:

  Usage:

  python predict.py –config=config.yaml –dataset=

  Example Command Line Invocation:

  python predict.py –config=config.yaml –dataset=./datasets

  Evaluation Script File Example Usage:

  Usage:

  python evaluate.py –config=config.yaml –metric=

  Example Command Line Invocation:

  python evaluate.py –config=config.yaml –metric=./metrics/accuracy.json

  cagriozel/autogluon<|file_sep|–! /usr/bin/env python3
  –! -*- coding:utf-8 -*-

  import os
  import json
  from pathlib import Path
  from collections import defaultdict
  from itertools import chain
  from functools import reduce
  from glob import glob
  from tqdm.auto import tqdm
  import torch

  PATH_ROOT_DIR=os.getcwd()
  PATH_CONFIG_DIR=os.path.join(PATH_ROOT_DIR,'configs')
  PATH_DATASET_DIR=os.path.join(PATH_ROOT_DIR,'datasets')
  PATH_MODELS_DIR=os.path.join(PATH_ROOT_DIR,'models')
  PATH_METRICS_DIR=os.path.join(PATH_ROOT_DIR,'metrics')

  if not os.access(PATH_CONFIG_DIR,isdir=True):
  raise Exception("Error occurred while trying access configuration files located at '{}'!".format(PATH_CONFIG_DIR))

  if not os.access(PATH_DATASET_DIR,isdir=True):
  raise Exception("Error occurred while trying access dataset located at '{}'!".format(PATH_DATASET_DIR))

  if not os.access(PATH_MODELS_DIR,isdir=True):
  raise Exception("Error occurred while trying access models located at '{}'!".format(PATH_MODELS_DIR))

  if not os.access(PATH_METRICS_DIR,isdir=True):
  raise Exception("Error occurred while trying access metrics located at '{}'!".format(PATH_METRICS_DIR))

  CONFIG_FILE_PATHS=glob('{}/*.yaml'.format(PATH_CONFIG_DIR))
  DATASET_FILE_PATHS=glob('{}/*'.format(PATH_DATASET_DIR))
  MODEL_FILE_PATHS=glob('{}/*'.format(PATH_MODELS_DIR))
  METRIC_FILE_PATHS=glob('{}/*.json'.format(PATH_METRICS_DIR))

  for CONFIG_FILE_PATH,DATASET_FILE_PATH,METRIC_FILE_PATH,MODEL_FILE_PATH
  in tqdm(zip(CONFIG_FILE_PATHS,DATASET_FILE_PATHS,METRIC_FILE_PATHS,MODEL_FILE_PATHS),desc='Processing Configurations'):
  print('33[K','33[A','33[A','33[A',end='r')

  try:
  print('33[K',"Loading configuration…",end='r')
  with open(CONFIG_FILE_PATH,'rt')as CONFIG_JSON:
  try:
  DATA_CONFIG=json.load(CONFIG_JSON)
  except json.JSONDecodeError:
  raise Exception('Invalid JSON formatting found!')

  except FileNotFoundError:
  raise Exception('Configuration does not exist!')

  try:
  print('33[K',"Loading dataset…",end='r')
  with open(D