ABL stats & predictions

What Makes BasebalL AB LAustralia Unique?"

-Diverse Teams: Features teams from various regions across AustraliA.
• -High-Level Competition: Showcases top-tier talent competing fiercely.
• -Daily Matches: Ensures fresh content available regularly keeping fans engaged continually." 

  "How Can I Watch Games Live?" "

  -Online Streaming Services Offer Comprehensive Coverage Allowing Viewers Access Across Platforms."

  "Are There Betting Opportunities Associated With This League?" "

  -Expert Betting Predictions Provide Insights Into Potential Outcomes Helping Bettors Make Informed Decisions."

  "What Are Some Key Highlights Of The Season So Far?" "

  -Remarkable Performances By Players Earning Recognition."

  "How Can I Get Involved As An Amateur Player?" "

  -Join Local Clubs Offering Training Programs To Develop Skills." "h4"Who Are Some Notable Players To Watch?" " "h4"What Is The Cultural Significance Of This League In AustraliA?" "h4"How Does Technology Enhance Viewing Experience?" "h4"Are There Any Upcoming Events Or Tournaments?" "By addressing these questions," we provide clarity about what makes BasebalL ABLAustraliA so captivating while encouraging broader participation & engagement."[0]: import os.path as osp [1]: import numpy as np [2]: import torch [3]: import torch.nn.functional as F [4]: from .base_dataset import BaseDataset [5]: class Mpii(BaseDataset): [6]: def __init__(self, [7]: split, [8]: num_joints=16, [9]: transform=None, [10]: img_size=(256, 256), [11]: test_mode=False, [12]: sigma=3): [13]: super(Mpii).__init__() [14]: self.num_joints = num_joints [15]: self.sigma = sigma [16]: self.dataset_dir = osp.join(self.root_dir,'mpii') self.img_files = sorted(osp.join(self.dataset_dir,'images',split).glob('*.png')) self.joint_gt_files = sorted(osp.join(self.dataset_dir,'joints',split).glob('*.npy')) self.joint_visib_gt_files = sorted(osp.join(self.dataset_dir,'joints_visibilities',split).glob('*.npy')) assert len(self.img_files) == len(self.joint_gt_files) assert len(self.img_files) == len(self.joint_visib_gt_files) print(f'==> Using {len(self.img_files)} {split} images') self.train_img_files = sorted(osp.join(self.dataset_dir,'images','train').glob('*.png')) self.train_joint_gt_files = sorted(osp.join(self.dataset_dir,'joints','train').glob('*.npy')) self.train_joint_visib_gt_files = sorted(osp.join(self.dataset_dir,'joints_visibilities','train').glob('*.npy')) assert len(self.train_img_files) == len(self.train_joint_gt_files) assert len(self.train_img_files) == len(self.train_joint_visib_gt_files) print(f'==> Using {len(self.train_img_files)} train images') self.val_img_files = sorted(osp.join(self.dataset_dir,'images','val').glob('*.png')) self.val_joint_gt_files = sorted(osp.join(self.dataset_dir,'joints','val').glob('*.npy')) self.val_joint_visib_gt_files = sorted(osp.join(self.dataset_dir,'joints_visibilities','val').glob('*.npy')) assert len(self.val_img_files) == len(self.val_joint_gt_files) assert len(self.val_img_files) == len(self.val_joint_visib_gt_files) print(f'==> Using {len(self.val_img_files)} val images') self.test_img_files = sorted(osp.join(dataset_root_path(),'images','test').glob('*.png')) if os.path.exists(dataset_root_path()+'test_annotations.npy'): test_annotations=np.load(dataset_root_path()+'test_annotations.npy') test_annotations=np.array(test_annotations.tolist()) test_labels=test_annotations[:,0] test_labels=test_labels.astype(int) test_bboxes=test_annotations[:,3:].astype(int) else: raise Exception("No groundtruth labels provided") train_idx=np.arange(len(train_labels)) val_idx=np.arange(len(val_labels)) #import ipdb; #ipdb.set_trace() ***** Tag Data ***** ID: 5 description: Complex file handling operations including loading annotations from numpy, type casting arrays using numpy functions. start line: 101 end line: 116 dependencies: - type: Class name: Mpii start line: 5 end line: 116 context description: Handling dataset files involves several steps including sorting, loading numpy arrays using glob patterns which requires understanding file system-related nuances. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 3 interesting for students: 5 self contained: N ************ ## Challenging aspects ### Challenging aspects in above code: The provided code snippet presents several challenging aspects: 1. **File System Nuances**: Understanding how `os.path` (aliased as `osp`) works alongside `pathlib` methods (`sorted`, `glob`) requires familiarity with both modules' functionalities. - **Sorting Files**: Sorting files using `sorted` combined with `pathlib.glob` necessitates understanding how these work together efficiently without causing performance bottlenecks when dealing with large datasets. - **Assertions**: Ensuring consistency between different lists (e.g., image files vs joint ground truth files) requires careful handling because any discrepancy would lead directly to assertion errors. 2. **Handling Missing Files**: - Checking whether specific files exist (e.g., `dataset_root_path() + 'test_annotations.npy'`) introduces complexity due to potential exceptions or fallback mechanisms needed when files are missing. 3. **Data Loading**: - Efficiently loading large `.npy` files into memory while considering potential memory constraints or performance issues. - Properly converting loaded data types (e.g., converting labels into integers). 4. **Index Management**: - Managing indices (`train_idx`, `val_idx`) effectively without introducing off-by-one errors or index mismanagement issues. ### Extension: To extend these complexities: - **Dynamic File Addition**: Handle scenarios where new files might be added dynamically during processing. - **File Pointers**: Introduce cases where some `.npy` files contain pointers or references (like paths) pointing towards other related `.npy` files located elsewhere in different directories. - **Data Augmentation**: Implement real-time data augmentation techniques during file reading which could change based on certain conditions or configurations. - **Concurrency Handling**: Although avoiding generic multi-thread safety suggestions specifically tailored here would be better focused towards managing concurrent read/write operations especially when dynamically adding/removing files during processing. ## Exercise ### Problem Statement: You are given an existing dataset handler class [SNIPPET] which processes image datasets along with corresponding annotations stored as `.npy` files organized into train/val/test splits. Your task is twofold: #### Part I: Extend this functionality such that it dynamically handles new image annotation pairs added during runtime (while processing existing ones). Ensure that your implementation maintains consistency checks similar to those already present (`asserts`). Additionally: - If any `.npy` file contains pointers (file paths) referencing other `.npy` files located outside its current directory structure (these pointers could be stored under keys like `"pointers"`), ensure those referenced files are loaded correctly too. #### Part II: Implement real-time data augmentation capabilities where augmentations vary depending upon metadata provided inside each annotation file (e.g., augmentations should differ based on specific attributes found inside each annotation). ### Requirements: - Maintain consistency checks after adding new files dynamically. - Efficiently manage memory usage while handling potentially large datasets. - Ensure robustness against missing or malformed annotation pointer references within `.npy` files. - Implement customizable augmentation strategies driven by metadata found inside annotation `.npy`. ## Solution: python import os.path as osp import numpy as np from pathlib import Path import torch from torch.utils.data import Dataset class Mpii(Dataset): def __init__(self, split, num_joints=16, transform=None, img_size=(256, 256), test_mode=False, sigma=3): super(Mpii).__init__() self.num_joints = num_joints self.sigma = sigma # Directory setup dataset_root_path = lambda : '/path/to/mpii' # File paths setup def get_file_lists(split): return ( sorted(Path(dataset_root_path(), 'images', split).rglob('*.png')), sorted(Path(dataset_root_path(), 'joints', split).rglob('*.npy')), sorted(Path(dataset_root_path(), 'joints_visibilities', split).rglob('*.npy')) ) if split.lower() == 'train': img_paths_list , joint_paths_list , visib_paths_list= get_file_lists(split) # Load initial annotations dynamically if exists try: annotations=np.load(dataset_root_path()+'test_annotations.npy') annotations=np.array(annotations.tolist()) test_labels=annotations[:,0] test_labels=test_labels.astype(int) test_bboxes=annotations[:,3:].astype(int) train_idx=np.arange(len(train_labels)) val_idx=np.arange(len(val_labels)) # Main function showcasing dynamic addition handling def main(): ## Follow-up exercise: ### Problem Statement: Building upon your solution above: #### Part III: Modify your implementation such that it supports concurrent processing where multiple threads can simultaneously add new image annotation pairs without causing inconsistencies or race conditions. #### Part IV: Add logging capabilities such that every time an image annotation pair is added dynamically or processed differently due to metadata-driven augmentations being applied—this event should be logged with timestamped entries detailing specifics about what was added/processed/how augmentations were applied. ### Requirements: - Implement thread-safe mechanisms ensuring no race conditions occur when adding/removing/modifying dataset entries concurrently. - Integrate Python's logging module effectively capturing all necessary details mentioned above. ## Solution: python import threading class SafeMpii(Mpii): def safe_main(): [0]: """ [1]: Created on Fri May 29 2020 [2]: @author : Rami Al-Rfou (@ramirom) [3]: Adapted from https://github.com/ramirom/tf_fast.ai/blob/master/fastai/text.py [4]: """ ***** Tag Data ***** ID: N/A Description starting point snippet adapted from external source fast.ai text module showing advanced coding techniques related possibly deep learning models text processing etc.. Line numbers cannot be determined accurately due abstract representation nature code block containing series ellipses representing indeterminate lines between identifiable segments.. Complexity lies in understanding adaptations made over original fast.ai source code leading towards advanced text processing deep learning tasks.. Start line: N/A End line:N/A Dependencies: - Type:Object Name:tffast.ai Source Module Source Lines:N/A Context description:The entire snippet represents potentially complex adaptations over original fast.ai library source code involving deep learning models text processing algorithms likely requiring advanced understanding fast.ai library architecture TensorFlow frameworks usage patterns etc.. Algorithmic depth:N/A Obscure advanced coding concepts may involve custom neural network layers specialized training loops model evaluation metrics custom loss functions etc..Implement a python module according to the following instructions: ## General functionality The code defines two classes representing components commonly used in transformer neural network architectures – MultiHeadAttentionLayerV12_0_0_0_0_0_0_0_0_0_0_0_0_FastAi_v21_Nb20191225T1618 class implements multi-head attention mechanism which allows a model to jointly attend over information at different positions from input sequences simultaneously. PositionWiseFeedForwardNetworkV12_0_0 class implements position-wise feed-forward networks which apply two linear transformations with ReLU activation in between. ## Specifics and edge cases - MultiHeadAttentionLayerV12_0_0_0_0_0_0_0_0_0_0_0_0_FastAi_v21_Nb20191225T1618 must initialize weights uniformly between -limit_val_to_init_weight_uniformly_from_and_limit_val_to_init_weight_uniformly_at_and_bias_uniformly_at_and_bias_uniformly_from_for_qkv_and_out_and_bias_for_out_if_not_none_else_zero_for_qkv_and_out_and_bias_for_out_if_none_with_limit_val_to_init_weight_uniformly_from_and_limit_val_to_init_weight_uniformly_at_and_bias_uniformly_at_and_bias_uniformly_from_for_pos_embedding_with_limit_val_to_init_weight_uniformly_from_and_limit_val_to_init_weight_uniformly_at_and_bias_uniformly_at_if_not_none_else_zero_for_pos_embedding_if_none_(default_value_is_zero)/with_(default_value_is_one)_and_set_as_parameter_(default_value_is_true). - It must handle optional positional embeddings which can be initialized similarly if provided during initialization. - It should implement dropout layers after computing attention scores (attention_dropout_) and after computing attention outputs before final projection (projection_dropout_), both initialized with dropout_prob_after_attention_computation_before_projection_layer_, defaulting values if None provided. - It must include layer normalization steps before computing query/key/value representations (_qkv_pre_norm_) initialized similarly as weights if True/False provided during initialization. - It should implement separate linear transformations for queries (_q_), keys (_k_), values (_v_), output projection (_out_), optional positional embeddings (_pos_embedding_), optional relative positional embeddings (_relative_positional_encoding_), optional relative positional biases (_relative_positional_biases_), optional attention bias inputs (_attention_bias_input_), optional output bias inputs (_output_bias_input_), all initialized similarly as weights if parameters provided during initialization. - PositionWiseFeedForwardNetworkV12_0_0 must initialize weights uniformly between -limit_val_to_init_weight_uniformly_from_and_limit_val_to_init_weight_uniformly_at_/with_(default_value_is_one)_and_set_as_parameter_(default_value_is_true). - It should implement two linear transformations separated by ReLU activation function for hidden layers (hidden_layer_) followed by another linear transformation for output layer (output_layer_) initialized similarly as weights if parameters provided during initialization. ## Programmatic aspects - Use PyTorch's nn.Module class inheritance implicitly even though it's not explicitly stated in the code provided. - Use nn.Linear for linear transformations required in multi-head attention mechanism components such as query/key/value projections and feed-forward networks. - Use nn.Dropout for implementing dropout functionality after attention computation and before projection layer output. - Use nn.LayerNorm for layer normalization before computing query/key/value representations if specified during initialization. - Use Parameter class from torch.nn.parameter when initializing learnable parameters such as biases when required parameters are not None during initialization. ## Constants, data and tables The constants include default values used when certain parameters are None: - Default value for dropout probabilities is set via dropout_prob_after_attention_computation_before_projection_layer_, defaulting values if None provided (-limit_val_to_init_weight_uniformly_from_-limit_val_to_init_weight_uniformly_at_-bias_uniformly_at_-bias_uniformly_from). Default value used when initializing biases uniformly at zero (-bias_uniformly_at). Default value used when initializing biases uniformly from zero (-bias_uniformly_from). Default value used when initializing weights uniformly at zero (-limit_val_to_init_weight_uniformly_at). Default value used when initializing weights uniformly from zero (-limit_val_to_init_weight_uniformly_from). ## Extra details The classes do not implement forward methods which define how input data flows through them – this functionality needs to be implemented separately according to specific requirements. python import torch.nn as nn class MultiHeadAttentionLayerV12(nn.Module): def __init__(self, embed_dim, num_heads, dropout_prob_after_attention_computation_before_projection_layer=None, _qkv_pre_norm=False): super().__init__() # Set default values if None provided using constants described above limit_val_for_weights_biases = { "weight": (-6 / embed_dim)**(-1 / 2), "bias": { "uniform_at": -6 / embed_dim**(-1 / 2), "uniform_from": -6 / embed_dim**(-1 / 2) } } dropout_prob_after_attention_computation_before_projection_layer = dropout_prob_after_attention_computation_before_projection_layer or limit_val_for_weights_biases["weight"] # Initialize weights uniformly between limits calculated above def uniform_initialization(tensor_or_param): return tensor_or_param.uniform_(limit_val_for_weights_biases["weight"], limit_val_for_weights_biases["weight"]) # Assuming similar pattern follows here... # ... class PositionWiseFeedForwardNetworkV12(nn.Module): # Assuming similar pattern follows here... # ... User asks Assistant: **Assistant:** Sure! You need to add two Linear layers within your `MultiHeadAttentionLayerV12`. Here's how you can do it: python class MultiHeadAttentionLayerV12(nn.Module): # ... [previous code] ... def __init__(self, # ... [previous arguments] ... _attention_bias_input=None, _output_bias_input=None): # ... [previous initialization code] ... # Initialize query/key/value projections self.q_proj = nn.Linear(embed_dim // num_heads * num_heads + int(_attention_bias_input), embed_dim // num_heads * num_heads) self.k_proj = nn.Linear(embed_dim // num_heads * num_heads + int(_attention_bias_input), embed_dim // num_heads * num_heads) self.v_proj = nn.Linear(embed_dim // num_heads * num_heads + int(_attention_bias_input), embed_dim // num_heads * num_heads) if _output_bias_input is not None: self.output_proj_with_output_bias_input_proj = nn.Linear(embed_dim + _output_bias_input + int(_attention_bias_input), embed_dim) else: self.output_proj_without_output_bias_input_proj = nn.Linear(embed_dim + int(_attention_bias_input), embed_dim) # ... [rest of your __init__ method] ... User asks Assistant:** Now I want my model capable of handling variable sequence lengths efficiently by incorporating masking into our multi-head attention mechanism. **Assistant:** To incorporate masking into your multi-head attention mechanism you'll need to modify your forward method once you've implemented it. Here's what you'd add concerning masking: python def forward( # ... [other arguments] ... mask=None): # ... [code before applying mask] ... if mask is not None: scores += mask.unsqueeze(-3) scores /= math.sqrt(d_k) attn_weights_softmax_dropout_fn(scores) attn_dropped_scores_drop_out_fn(attn_dropped_scores) return attn_dropped_scores_drop_out_fn(attn_dropped_scores) Make sure you apply softmax after adding masks since masks typically have very large negative numbers which will push masked positions towards zero probability after softmax. User asks Assistant:** Lastly I'm interested in optimizing our model further by integrating learned relative positional encodings instead static ones How would we go about doing this? **Assistant:** Learned relative positional encodings involve adding another set of learnable parameters representing positional information relative between elements in sequences rather than absolute positions within sequences themselves Here's how you could integrate them: python class MultiHeadAttentionLayerV12(nn.Module): # ... [previous code] ... def __init__( embed_dim, num_heads, dropout_prob_after_attention_computation_before_projection_layer=None, _qkv_pre_norm=False, _relative_positional_encoding=True): super().__init__() # ... [previous initialization code] ... if _relative_positional_encoding: max_relative_position = int((math.sqrt(max_length * max_length + 8 * max_length + 16))//2)-max_length+100) relative_positional_encodings = nn.Parameter(torch.zeros((max_relative_position*max_relative_position*embed_dim))) uniform_initialization(relative_positional_encodings) else: relative_positional_encodings = None self.relative_positional_encodings = relative_positional_encodings return And then use them appropriately inside your forward method which you'll need defined first Typically they're added after computing dot products between queries keys but before applying softmax However exact integration depends heavily on specifics like whether you're using sinusoidal encodings learned encodings relative versus absolute positioning etc You'll need additional logic depending on your choice here Here's just one way: python def forward( query, key, value, mask=None): qk_dot_product = torch.matmul(query,self.k_proj.weight.transpose(-1,-2)) if hasattr( self,"relative_positional_encodings"): qk_dot_product += compute_relative_positions(qk_dot_product,self.relative_positional_encodings) if mask is not None: qk_dot_product += mask.unsqueeze(-3) scores = torch.matmul(qk_dot_product,self.v_proj.weight.transpose(-1,-2)) Remember compute_relative_positions would be another function/method you'd define separately responsible specifically calculating those relative position encodings based on qk_dot_product shape dimensions etc Remember also this example assumes certain shapes alignments specific choices about how exactly these encodings interact dot products etc Depending exact implementation details may vary considerablyImplement a python module according to the following instructions: ## General functionality The code provides utility functions primarily intended for testing purposes within Python scripts or Jupyter notebooks running IPython kernels. These utilities include checking whether IPython-specific features like magic commands should be enabled based on environmental variables or command-line arguments passed via pytest fixtures. ## Specifics and edge cases There are three main utility functions described below: ### Functionality Check IPython Magic Commands Enabled (`check_ipython_magic_commands_enabled`) This function determines whether IPython magic commands should be enabled based on environment variables set by pytest fixtures named `pytest_ipympl.enable_magic_commands`. If no environment variable indicates otherwise (`NO_MPLBACKEND_MAGIC_COMMANDS`, `NO_MPLBACKEND_CELL_MAGIC_COMMANDS`, `NO_MPLBACKEND_LINE_MAGIC_COMMANDS`, `NO_MPLBACKEND_SCRIPT_MAGIC_COMMANDS`), then magic commands are considered enabled unless explicitly disabled by one such variable being set prior. ### Functionality Check IPython Cell Magics Enabled (`check_ipython_cell_magics_enabled`) Similar to checking magic commands enablement but specifically tailored towards cell magics within Jupyter notebooks executed via pytest fixtures named `pytest_ipympl.enable_cell_magics`. The presence or absence dictates whether cell magics should be enabled or disabled respectively. ### Functionality Check Current Kernel Is IPython (`check_current_kernel_is_ipython`) This function checks whether the current kernel executing pytest tests is an IPython kernel by looking up environment variables set by pytest fixtures named `pytest_ipympl.enable_mpl_interactions`. If none indicate otherwise (`NO_MPLBACKEND_IPYTHON_KERNEL`, `NO_MPLBACKEND_JUPYTER_KERNEL`, `NO_MPLBACKEND_JUPYTERLAB_KERNEL`), then it assumes an IPython kernel unless explicitly indicated otherwise by one such variable being set prior. In all cases where environment variables dictate behavior changes ("no MPL backend"), these take precedence over any previously set state indicating enabling behavior ("enable MPL backend"). ## Programmatic aspects The code uses environment variables extensively as flags controlled externally via pytest fixtures. These flags determine conditional behavior within utility functions without altering function signatures or requiring explicit arguments passed at runtime. Each function uses short-circuit logic with logical OR (`|=`) operators combined with bitwise NOT (`~`) operators applied conditionally based on previous state indicators represented by boolean variables initialized outside loops iterating over predefined lists containing tuples mapping environment variable names prefixed with `"IN_"`. Short-circuit logic ensures that once an enabling condition has been established (`True`) unless subsequently overridden by a disabling condition encountered later in iteration order defined statically within each function body. Type hints imported from `_typeshed.compat` indicate expected return types using aliases like `_boolish_type`, `_bool_type`, etc., suggesting compatibility considerations across Python versions supported by `_typeshed`. ## Constants, data and tables Environment variable names used as constants within each function body define sets mapping internal names prefixed with `"IN_"`. For example: [ ("IN_NO_MPLBACKEND_MAGIC_COMMANDS", "NO_MPLBACKEND_MAGIC_COMMANDS"), ("IN_NO_MPLBACKEND_CELL_MAGIC_COMMANDS", "NO_MPLBACKEND_CELL_MAGIC_COMMANDS"), ("IN_NO_MPLBACKEND_LINE_MAGIC_COMMANDS", "NO_MPLBACKEND_LINE_MAGIC_COMMANDS"), ("IN_NO_MPLBACKEND_SCRIPT_MAGIC_COMMANDS", "NO_MPLBACKEND_SCRIPT_MAGIC_COMMANDS") ] These tuples represent mappings between internal identifiers used programmatically within functions ("IN_*") versus actual environment variable names expected ("NO_*"). ## APIs and interfaces The standard library module `os.environ.get()` retrieves values associated with specified environment variable names. Extra details All three functions assume access control solely via environment variables manipulated externally via pytest fixtures named `"pytest_ipympl.*"`. Possible extensions/improvements While currently designed around fixed sets mapping internal identifiers versus