Betting List 1: Goal Scoring Predictions

Historically, matches involving these two clubs have seen an average of around 1.8 goals per game, indicating that a scoreline exceeding 1.5 goals is plausible.

Betting List 2: Over/Under Goals Prediction

• Section 1: Over 2.5 goals prediction
  • The over/under goal predictions lean towards an under 2.5 goals outcome given recent defensive stances observed in MFK Karviná’s matches combined with FC Zlin’s solid backline performances.

Betting List 3: Both Teams to Score Prediction

• Section 1: Both Teams to Score Yes

  In assessing this sporting event, given the odds of each individual element within this paragraph.
  With these points considered, we see potential for an exciting match-up between the clubs’ aggressive offense and dynamic play styles.

  [0]: import os
  [1]: import os
  [2]: import numpy as np

  [6]: from os.path import join as pjoin
  [3]: from collections import OrderedDict

  [4]: from models.pytorch_transformers.tokenization_utils import load_vocab_from_file

  [5]: def load_labels(file_path):
  [6]: labels = []
  [7]: with open(file_path=file_path) as f:
  [8]: lines = f.readlines()
  [9]: # lines = file.readlines()
  [10]: for line in lines:
  [11]: label = line.split(“t”)
  [12]: if label not in labels:
  [13]: labels.append(label)
  [14]: return labels

  [15]: class BertData(object):

  [16]: def __init__(self,
  [17]: data_path,
  [18]: train_file=None,
  [19]: eval_file=None,
  [20]: batch_size=128,
  [21]: seq_len=128,
  [22]: max_pred=80,
  [23]: short_seq_prob=0.1,
  [24]: dupe_factor=5,
  [25]: small_seq_prob=0.01,
  [26]: masked_lm_prob=0.15,
  [27]: max_seq_len=512,
  [28]: do_pair=False,

  ***** Tag Data *****
  ID: 4
  description: Class definition `BertData` initialization with multiple parameters.
  start line: 16
  end line: 27
  dependencies:
  – type: Class
  name: BertData
  start line: 15
  end line: 15
  algorithmic depth: 4
  algorithmic depth external: N
  context description: The class `BertData` initializes various parameters which are
  used later in configuring how data is loaded and processed.
  context description: This snippet initializes an instance of `BertData`, setting
  up several parameters that will influence how data is preprocessed and manipulated.
  start line: 16
  end line: 28
  dependencies:
  – type: Class
  name: BertDataLoader
  start line: 16
  end line: 27
  – The snippet shows that this class sets up multiple parameters that affect how data is processed.
  algorithmic depth external dependencies:
  – type: Function/Method
  description:
  – The __init__ method of BertData is responsible for initializing multiple configurations.
  algorithmic depth algorithmic depth external:
  – type: Function/Method
  name: __init__
  start line: 16
  end line: 27

  ************
  ## Challenging aspects

  ### Challenging aspects in above code:

  1. **Parameter Initialization**: The initialization involves setting up multiple parameters which are crucial for data processing (`batch_size`, `seq_len`, `max_pred`, etc.). Understanding how each parameter influences the overall data processing pipeline is critical.

  2. **Handling Variable Length Sequences**: The `seq_len` parameter implies that sequences can have different lengths up to `max_seq_len`. Efficiently managing variable-length sequences requires careful handling to avoid memory issues and ensure proper batching.

  3. **Probability Parameters**: Parameters like `short_seq_prob`, `small_seq_prob`, and `masked_lm_prob` introduce randomness into the data processing pipeline which can make debugging challenging.

  4. **Duplicated Data Factor**: The `dupe_factor` parameter suggests that some data may be duplicated multiple times during processing. Managing these duplicates without redundancy or inefficiency adds another layer of complexity.

  5. **Pairwise Data Handling**: The `do_pair` flag indicates whether data should be processed in pairs or individually, which changes how sequences are batched and processed.

  6. **File Handling**: The presence of file paths (`data_path`, `train_file`) implies that file I/O operations are involved, adding complexity related to file reading/writing efficiency and error handling.

  ### Extension:

  1. **Dynamic Data Augmentation**: Extend the functionality to dynamically augment data based on specific rules or probabilities during runtime.

  2. **Advanced Probability Management**: Introduce more sophisticated probability management techniques that adapt based on training progress or specific conditions (e.g., adaptive masking probability).

  3. **Parallel Processing**: Implement parallel processing capabilities to handle large datasets more efficiently while ensuring thread safety and consistency.

  4. **Sequence Alignment**: Add functionality to align sequences based on certain criteria before processing them (e.g., aligning sequences based on token similarity).

  5. **Custom Data Loading Strategies**: Allow users to define custom strategies for loading and preprocessing data, making the system more flexible and adaptable to different use cases.

  ## Exercise

  ### Problem Statement:

  Expand the [SNIPPET] provided by implementing an advanced data preprocessing class `AdvancedBertData` that inherits from `BertData`. This new class should include:

  1. Dynamic Data Augmentation:
  – Implement methods that augment data dynamically based on user-defined rules during runtime.
  – Include at least three types of augmentations (e.g., synonym replacement, random insertion, random swap).

  2. Adaptive Masking Probability:
  – Modify the masking probability (`masked_lm_prob`) dynamically based on training epoch or loss value.
  – Ensure that this probability can be adjusted via a user-defined schedule.

  3. Sequence Alignment:
  – Implement functionality to align sequences based on token similarity before processing them.
  – Use cosine similarity as a metric for alignment.

  4. Custom Data Loading Strategies:
  – Allow users to pass custom functions for loading and preprocessing data.
  – Ensure these custom functions can integrate seamlessly with existing initialization parameters.

  ### Requirements:

  – Your implementation should maintain compatibility with the original `BertData` initialization parameters.
  – Ensure efficient memory management when handling variable-length sequences.
  – Include comprehensive error handling for file I/O operations.
  – Provide unit tests demonstrating the functionality of each new feature.

  ## Solution

  python
  import numpy as np

  class BertData(object):
  def __init__(self,
  data_path,
  train_file=None,
  eval_file=None,
  batch_size=128,
  seq_len=128,
  max_pred=80,
  short_seq_prob=0.1,
  dupe_factor=5,
  small_seq_prob=0.01,
  masked_lm_prob=0.15,
  max_seq_len=512,
  do_pair=False):
  self.data_path = data_path
  self.train_file = train_file
  self.eval_file = eval_file
  self.batch_size = batch_size
  self.seq_len = seq_len
  self.max_pred = max_pred
  self.short_seq_prob = short_seq_prob
  self.dupe_factor = dupe_factor
  self.small_seq_prob = small_seq_prob
  self.masked_lm_prob = masked_lm_prob
  self.max_seq_len = max_seq_len
  self.do_pair = do_pair

  class AdvancedBertData(BertData):
  def __init__(self, *args, augmentation_rules=None, masking_schedule=None, custom_loader=None, **kwargs):
  super().__init__(*args, **kwargs)

  self.augmentation_rules = augmentation_rules if augmentation_rules else []
  self.masking_schedule = masking_schedule if masking_schedule else lambda epoch: self.masked_lm_prob

  if custom_loader:
  self.custom_loader = custom_loader

  def augment_data(self, sequence):
  augmented_sequence = sequence.copy()

  # Apply augmentation rules dynamically here

  if ‘synonym_replacement’ in self.augmentation_rules:
  augmented_sequence = self.synonym_replacement(augmented_sequence)

  if ‘random_insertion’ in self.augmentation_rules:
  augmented_sequence = self.random_insertion(augmented_sequence)

  if ‘random_swap’ in self.augmentation_rules:
  augmented_sequence = self.random_swap(augmented_sequence)

  return augmented_sequence

  def synonym_replacement(self, sequence):
  # Implement synonym replacement logic here

  return sequence

  def random_insertion(self, sequence):
  # Implement random insertion logic here

  return sequence

  def random_swap(self, sequence):
  # Implement random swap logic here

  return sequence

  def update_masking_probability(self, epoch):
  # Update masking probability based on schedule

  self.masked_lm_prob = self.masking_schedule(epoch)

  def align_sequences(self, sequences):
  aligned_sequences = []

  # Implement sequence alignment logic using cosine similarity

  return aligned_sequences

  def load_data(self):

  if hasattr(self, ‘custom_loader’):
  return self.custom_loader(self.data_path)

  # Default file loading logic here

  # Example usage:

  def custom_data_loader(data_path):
  # Custom data loading logic here

  return []

  augmentation_rules = [‘synonym_replacement’, ‘random_insertion’, ‘random_swap’]
  masking_schedule = lambda epoch: min(0.15 + epoch * 0.01, 0.35)

  advanced_data_processor = AdvancedBertData(
  data_path=”path/to/data”,
  train_file=”train.txt”,
  augmentation_rules=augmentation_rules,
  masking_schedule=masking_schedule,
  custom_loader=custom_data_loader)

  # Unit tests can be written using unittest or pytest frameworks

  ## Follow-up exercise

  ### Problem Statement:

  Extend your implementation of `AdvancedBertData` by adding support for multi-threaded data loading while ensuring thread safety and consistency across batches.

  Additionally:

  – Implement a mechanism to monitor memory usage during training and adjust batch sizes dynamically if memory usage exceeds a certain threshold.
  – Introduce a logging mechanism that logs detailed information about each step of the preprocessing pipeline (e.g., augmentation applied, masking probability updates).

  ### Requirements:

  – Ensure thread safety when implementing multi-threaded data loading.
  – Provide detailed logging information at each step.
  – Demonstrate memory monitoring and dynamic adjustment of batch sizes through unit tests.

  ## Solution

  python
  import threading
  import logging

  class AdvancedBertDataWithLogging(AdvancedBertData):

  def __init__(self, *args, log_level=logging.INFO, memory_threshold=1024*1024*1024*8, **kwargs): # Default threshold set to ~8GB
  super().__init__(*args, **kwargs)

  logging.basicConfig(level=log_level)

  self.memory_threshold = memory_threshold

  def load_data_threaded(self):

  # Multi-threaded loading logic here using threading.Thread()

  threads = []

  # Example thread creation (more complex logic needed)

  t = threading.Thread(target=self.load_data)
  threads.append(t)
  t.start()

  for t in threads:
  t.join()

  def monitor_memory_and_adjust_batch_size(self):

  # Monitor memory usage logic here

  current_memory_usage = … # Retrieve current memory usage

  if current_memory_usage > self.memory_threshold:
  logging.warning(“Memory usage exceeded threshold! Adjusting batch size.”)
  self.batch_size //= 2

  def log_step_info(self, step_info):

  logging.info(step_info)

  # Example usage:

  advanced_data_processor_with_logging = AdvancedBertDataWithLogging(
  data_path=”path/to/data”,
  train_file=”train.txt”,
  augmentation_rules=augmentation_rules,
  masking_schedule=masking_schedule,
  custom_loader=custom_data_loader)

  # Implement unit tests for multi-threaded loading & memory monitoring

  This extended exercise pushes students to consider multi-threading complexities such as race conditions and deadlocks while also ensuring efficient resource management through dynamic adjustments based on real-time monitoring.
  *** Excerpt ***

  The first two examples demonstrate a fairly simple process where the value is passed through one or two transformations before it reaches its final value on screen (or its final value used within another calculation). These are relatively simple processes that don’t involve any complicated calculations or considerations beyond what’s outlined above.
  However there are many cases where this isn’t true; where there are many transformations being applied to a single value before it reaches its final state – often these involve some kind of interpolation where several values contribute towards the final value displayed – which can make things considerably more complicated!
  In order to understand how all these transformations work together let’s look at an example involving four transformations; three being applied sequentially (a linear interpolation between two values), followed by another transformation being applied afterwards (a quadratic curve). This example should give you an idea about how all these different types fit together – even though it doesn’t cover every possible scenario!
  First let’s take a look at our initial value – which will be transformed through each step until we reach our final result:

  *** Revision 0 ***

  ## Plan

  To create an advanced exercise that challenges deep understanding and incorporates additional factual knowledge beyond what’s provided in the excerpt:

  1. Introduce terminology specific to mathematical transformations or computer graphics rendering processes—such as “bilinear interpolation,” “gamma correction,” or “perspective transformation”—to require additional domain knowledge.

  2. Embed logical deductions within the process described—such as inferring properties about intermediate steps based on final outcomes or vice versa—and require understanding how certain transformations affect others when combined.

  3. Include nested counterfactuals and conditionals—such as discussing what would happen under different transformation sequences or varying parameters within those transformations—to test comprehension of complex hypothetical scenarios.

  By doing so, we not only test reading comprehension but also assess whether the reader can apply complex mathematical concepts and logical reasoning skills within hypothetical scenarios.

  ## Rewritten Excerpt

  {“MAIN_EXCERPT”: “Consider initial instances where a datum undergoes mere linear transformations prior to its manifestation—either visually or computationally—in its ultimate form; these instances entail rudimentary manipulations devoid of intricate computations or elaborate considerations beyond those delineated hitherto.nNonetheless, myriad scenarios exist wherein multiple transformations converge upon a solitary datum prior to its consummation—a process often entailing interpolative mechanisms whereby disparate values amalgamate into one definitive representation—thus engendering significant complexity.nTo elucidate upon this intricate web of transformations let us examine an exemplar comprising four discrete yet interrelated transformations; initially three interpolative transformations occur sequentially—a bilinear interpolation between quadrants followed by two separate linear interpolations—and subsequently a fourth transformation ensues—a non-linear distortion via quadratic mapping.nThis paradigm serves not only as an illustration but also as an intellectual probe into understanding how disparate transformational operations coalesce—even though it does not exhaustively enumerate every conceivable permutation.nCommence with our primordial datum—subjected sequentially through each transformative stage until we arrive at our denouement:”}

  ## Suggested Exercise

  Consider the following scenario derived from advanced computer graphics rendering processes:

  A single datum begins its journey through a series of complex transformations before reaching its final visual representation on screen:

  1) It undergoes bilinear interpolation between four distinct quadrant values Q1 through Q4—each representing color intensities at respective vertices of a textured plane.

  2) Following this step comes two sequential linear interpolations along orthogonal axes within this plane—first along one axis (x-axis), then