id int64 0 328k | repository_name stringlengths 7 58 | file_path stringlengths 9 302 | class_name stringlengths 5 256 | human_written_code stringlengths 16 2.16M | class_skeleton stringlengths 18 1.49M ⌀ | total_program_units int64 1 1.76k | total_doc_str int64 0 771 | AvgCountLine float64 0 7.89k | AvgCountLineBlank float64 0 297 | AvgCountLineCode float64 0 7.89k | AvgCountLineComment float64 0 7.89k | AvgCyclomatic float64 0 130 | CommentToCodeRatio float64 0 168 | CountClassBase float64 0 40 | CountClassCoupled float64 0 583 | CountClassCoupledModified float64 0 575 | CountClassDerived float64 0 5.35k | CountDeclInstanceMethod float64 0 529 | CountDeclInstanceVariable float64 0 296 | CountDeclMethod float64 0 599 | CountDeclMethodAll float64 0 1.12k | CountLine float64 1 40.4k | CountLineBlank float64 0 8.16k | CountLineCode float64 1 25.7k | CountLineCodeDecl float64 1 8.15k | CountLineCodeExe float64 0 24.2k | CountLineComment float64 0 16.5k | CountStmt float64 1 9.71k | CountStmtDecl float64 1 8.15k | CountStmtExe float64 0 9.69k | MaxCyclomatic float64 0 759 | MaxInheritanceTree float64 0 16 | MaxNesting float64 0 34 | SumCyclomatic float64 0 2.9k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
326,400 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/compilation/agents_compiler.py | apm_cli.compilation.agents_compiler.AgentsCompiler | import datetime
from ..primitives.discovery import discover_primitives
from pathlib import Path
from ..version import get_version
from typing import List, Optional, Dict, Any
from .link_resolver import resolve_markdown_links, validate_link_targets
from .template_builder import build_conditional_sections, generate_agent... |
class AgentsCompiler:
'''Main compiler for generating AGENTS.md files.'''
def __init__(self, base_dir: str='.'):
'''Initialize the compiler.
Args:
base_dir (str): Base directory for compilation. Defaults to current directory.
'''
pass
def compile(self, config: ... | 8 | 8 | 27 | 4 | 14 | 8 | 3 | 0.61 | 0 | 11 | 4 | 0 | 7 | 3 | 7 | 7 | 195 | 37 | 98 | 33 | 90 | 60 | 70 | 30 | 62 | 9 | 0 | 4 | 23 |
326,401 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/compilation/agents_compiler.py | apm_cli.compilation.agents_compiler.CompilationConfig | from dataclasses import dataclass
from typing import List, Optional, Dict, Any
from pathlib import Path
@dataclass
class CompilationConfig:
"""Configuration for AGENTS.md compilation."""
output_path: str = 'AGENTS.md'
chatmode: Optional[str] = None
resolve_links: bool = True
dry_run: bool = False
... | @dataclass
class CompilationConfig:
'''Configuration for AGENTS.md compilation.'''
@classmethod
def from_apm_yml(cls, **overrides) -> 'CompilationConfig':
'''Create configuration from apm.yml with command-line overrides.
Args:
**overrides: Command-line arguments that override con... | 4 | 2 | 41 | 9 | 21 | 12 | 8 | 0.48 | 0 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 49 | 10 | 27 | 14 | 22 | 13 | 26 | 12 | 22 | 8 | 0 | 3 | 8 |
326,402 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/compilation/agents_compiler.py | apm_cli.compilation.agents_compiler.CompilationResult | from typing import List, Optional, Dict, Any
from dataclasses import dataclass
@dataclass
class CompilationResult:
"""Result of AGENTS.md compilation."""
success: bool
output_path: str
content: str
warnings: List[str]
errors: List[str]
stats: Dict[str, Any] | @dataclass
class CompilationResult:
'''Result of AGENTS.md compilation.'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 7 | 1 | 6 | 1 | 7 | 1 | 6 | 0 | 0 | 0 | 0 |
326,403 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/compilation/template_builder.py | apm_cli.compilation.template_builder.TemplateData | from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass
@dataclass
class TemplateData:
"""Data structure for template generation."""
instructions_content: str
timestamp: str
version: str
chatmode_content: Optional[str] = None | @dataclass
class TemplateData:
'''Data structure for template generation.'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 5 | 2 | 4 | 1 | 5 | 2 | 4 | 0 | 0 | 0 | 0 |
326,404 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/core/script_runner.py | apm_cli.core.script_runner.PromptCompiler | from pathlib import Path
from typing import Dict, Optional
class PromptCompiler:
"""Compiles .prompt.md files with parameter substitution."""
def __init__(self):
"""Initialize compiler."""
self.compiled_dir = Path('.apm/compiled')
def compile(self, prompt_file: str, params: Dict[str, str]... |
class PromptCompiler:
'''Compiles .prompt.md files with parameter substitution.'''
def __init__(self):
'''Initialize compiler.'''
pass
def compile(self, prompt_file: str, params: Dict[str, str]) -> str:
'''Compile a .prompt.md file with parameter substitution.
Args:
... | 4 | 4 | 21 | 4 | 10 | 8 | 2 | 0.77 | 0 | 3 | 0 | 0 | 3 | 1 | 3 | 3 | 69 | 14 | 31 | 17 | 27 | 24 | 29 | 16 | 25 | 4 | 0 | 2 | 7 |
326,405 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/core/script_runner.py | apm_cli.core.script_runner.ScriptRunner | from typing import Dict, Optional
import re
from pathlib import Path
import subprocess
import yaml
class ScriptRunner:
"""Executes APM scripts with auto-compilation of .prompt.md files."""
def __init__(self, compiler=None):
"""Initialize script runner with optional compiler."""
self.compiler =... |
class ScriptRunner:
'''Executes APM scripts with auto-compilation of .prompt.md files.'''
def __init__(self, compiler=None):
'''Initialize script runner with optional compiler.'''
pass
def run_script(self, script_name: str, params: Dict[str, str]) -> bool:
'''Run a script from apm... | 7 | 7 | 27 | 5 | 14 | 9 | 5 | 0.6 | 0 | 8 | 1 | 0 | 6 | 1 | 6 | 6 | 172 | 33 | 87 | 35 | 79 | 52 | 79 | 31 | 72 | 15 | 0 | 4 | 27 |
326,406 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/factory.py | apm_cli.factory.ClientFactory | from .adapters.client.vscode import VSCodeClientAdapter
class ClientFactory:
"""Factory for creating MCP client adapters."""
@staticmethod
def create_client(client_type):
"""Create a client adapter based on the specified type.
Args:
client_type (str): Type of client adapter to... |
class ClientFactory:
'''Factory for creating MCP client adapters.'''
@staticmethod
def create_client(client_type):
'''Create a client adapter based on the specified type.
Args:
client_type (str): Type of client adapter to create.
Returns:
MCPClientAdapter: An... | 3 | 2 | 21 | 5 | 7 | 9 | 2 | 1.11 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 1 | 25 | 6 | 9 | 4 | 6 | 10 | 6 | 3 | 4 | 2 | 0 | 1 | 2 |
326,407 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/factory.py | apm_cli.factory.PackageManagerFactory | from .adapters.package_manager.default_manager import DefaultMCPPackageManager
class PackageManagerFactory:
"""Factory for creating MCP package manager adapters."""
@staticmethod
def create_package_manager(manager_type='default'):
"""Create a package manager adapter based on the specified type.
... |
class PackageManagerFactory:
'''Factory for creating MCP package manager adapters.'''
@staticmethod
def create_package_manager(manager_type='default'):
'''Create a package manager adapter based on the specified type.
Args:
manager_type (str, optional): Type of package manager ad... | 3 | 2 | 22 | 5 | 7 | 10 | 2 | 1.22 | 0 | 2 | 1 | 0 | 0 | 0 | 1 | 1 | 26 | 6 | 9 | 4 | 6 | 11 | 6 | 3 | 4 | 2 | 0 | 1 | 2 |
326,408 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/primitives/models.py | apm_cli.primitives.models.Chatmode | from typing import Optional, List, Union
from dataclasses import dataclass
from pathlib import Path
@dataclass
class Chatmode:
"""Represents a chatmode primitive."""
name: str
file_path: Path
description: str
apply_to: Optional[str]
content: str
author: Optional[str] = None
version: Opt... | @dataclass
class Chatmode:
'''Represents a chatmode primitive.'''
def validate(self) -> List[str]:
'''Validate chatmode structure.
Returns:
List[str]: List of validation errors.
'''
pass | 3 | 2 | 12 | 1 | 7 | 4 | 3 | 0.4 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 22 | 2 | 15 | 5 | 13 | 6 | 15 | 5 | 13 | 3 | 0 | 1 | 3 |
326,409 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/primitives/models.py | apm_cli.primitives.models.Context | from pathlib import Path
from typing import Optional, List, Union
from dataclasses import dataclass
@dataclass
class Context:
"""Represents a context primitive."""
name: str
file_path: Path
content: str
description: Optional[str] = None
author: Optional[str] = None
version: Optional[str] = ... | @dataclass
class Context:
'''Represents a context primitive.'''
def validate(self) -> List[str]:
'''Validate context structure.
Returns:
List[str]: List of validation errors.
'''
pass | 3 | 2 | 10 | 1 | 5 | 4 | 2 | 0.42 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 19 | 2 | 12 | 6 | 10 | 5 | 12 | 6 | 10 | 2 | 0 | 1 | 2 |
326,410 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/primitives/models.py | apm_cli.primitives.models.Instruction | from pathlib import Path
from typing import Optional, List, Union
from dataclasses import dataclass
@dataclass
class Instruction:
"""Represents an instruction primitive."""
name: str
file_path: Path
description: str
apply_to: str
content: str
author: Optional[str] = None
version: Option... | @dataclass
class Instruction:
'''Represents an instruction primitive.'''
def validate(self) -> List[str]:
'''Validate instruction structure.
Returns:
List[str]: List of validation errors.
'''
pass | 3 | 2 | 14 | 1 | 9 | 4 | 4 | 0.35 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 24 | 2 | 17 | 5 | 15 | 6 | 17 | 5 | 15 | 4 | 0 | 1 | 4 |
326,411 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/primitives/models.py | apm_cli.primitives.models.PrimitiveCollection | from typing import Optional, List, Union
from dataclasses import dataclass
@dataclass
class PrimitiveCollection:
"""Collection of discovered primitives."""
chatmodes: List[Chatmode]
instructions: List[Instruction]
contexts: List[Context]
def __init__(self):
self.chatmodes = []
self... | @dataclass
class PrimitiveCollection:
'''Collection of discovered primitives.'''
def __init__(self):
pass
def add_primitive(self, primitive: Primitive) -> None:
'''Add a primitive to the appropriate collection.'''
pass
def all_primitives(self) -> List[Primitive]:
'''Ge... | 6 | 4 | 5 | 0 | 4 | 1 | 2 | 0.19 | 0 | 6 | 3 | 0 | 4 | 0 | 4 | 4 | 29 | 4 | 21 | 5 | 16 | 4 | 18 | 5 | 13 | 4 | 0 | 1 | 7 |
326,412 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/registry/client.py | apm_cli.registry.client.SimpleRegistryClient | import os
from typing import Dict, List, Optional, Any, Tuple
import requests
class SimpleRegistryClient:
"""Simple client for querying MCP registries for server discovery."""
def __init__(self, registry_url: Optional[str]=None):
"""Initialize the registry client.
Args:
registry_u... |
class SimpleRegistryClient:
'''Simple client for querying MCP registries for server discovery.'''
def __init__(self, registry_url: Optional[str]=None):
'''Initialize the registry client.
Args:
registry_url (str, optional): URL of the MCP registry.
If not provided, u... | 7 | 7 | 23 | 5 | 9 | 9 | 3 | 1.04 | 0 | 6 | 0 | 0 | 6 | 2 | 6 | 6 | 149 | 37 | 55 | 24 | 48 | 57 | 49 | 24 | 42 | 6 | 0 | 3 | 16 |
326,413 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/registry/integration.py | apm_cli.registry.integration.RegistryIntegration | from typing import Dict, List, Any, Optional
from .client import SimpleRegistryClient
import requests
class RegistryIntegration:
"""Integration class for connecting registry discovery to package manager."""
def __init__(self, registry_url: Optional[str]=None):
"""Initialize the registry integration.
... |
class RegistryIntegration:
'''Integration class for connecting registry discovery to package manager.'''
def __init__(self, registry_url: Optional[str]=None):
'''Initialize the registry integration.
Args:
registry_url (str, optional): URL of the MCP registry.
If not... | 8 | 8 | 20 | 4 | 8 | 8 | 3 | 0.98 | 0 | 5 | 1 | 0 | 7 | 1 | 7 | 7 | 147 | 32 | 58 | 20 | 50 | 57 | 50 | 20 | 42 | 7 | 0 | 3 | 20 |
326,414 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/runtime/base.py | apm_cli.runtime.base.RuntimeAdapter | from typing import Dict, Any, Optional
from abc import ABC, abstractmethod
class RuntimeAdapter(ABC):
"""Base adapter interface for LLM runtimes."""
@abstractmethod
def execute_prompt(self, prompt_content: str, **kwargs) -> str:
"""Execute a single prompt and return the response.
Args:
... |
class RuntimeAdapter(ABC):
'''Base adapter interface for LLM runtimes.'''
@abstractmethod
def execute_prompt(self, prompt_content: str, **kwargs) -> str:
'''Execute a single prompt and return the response.
Args:
prompt_content: The prompt text to execute
**kwargs: Ad... | 14 | 7 | 7 | 1 | 2 | 4 | 1 | 1.25 | 1 | 3 | 0 | 2 | 4 | 0 | 6 | 26 | 57 | 12 | 20 | 12 | 6 | 25 | 13 | 7 | 6 | 1 | 4 | 0 | 6 |
326,415 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/runtime/codex_runtime.py | apm_cli.runtime.codex_runtime.CodexRuntime | import subprocess
from typing import Dict, Any, Optional
from .base import RuntimeAdapter
import shutil
class CodexRuntime(RuntimeAdapter):
"""APM adapter for the Codex CLI."""
def __init__(self, model_name: Optional[str]=None):
"""Initialize Codex runtime.
Args:
model_name: Model... |
class CodexRuntime(RuntimeAdapter):
'''APM adapter for the Codex CLI.'''
def __init__(self, model_name: Optional[str]=None):
'''Initialize Codex runtime.
Args:
model_name: Model name (not used for Codex, included for compatibility)
'''
pass
def execute_prompt(s... | 10 | 7 | 19 | 3 | 11 | 6 | 3 | 0.51 | 1 | 8 | 0 | 0 | 5 | 1 | 7 | 33 | 141 | 25 | 79 | 23 | 67 | 40 | 47 | 18 | 37 | 8 | 5 | 3 | 18 |
326,416 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/runtime/factory.py | apm_cli.runtime.factory.RuntimeFactory | from .base import RuntimeAdapter
from typing import List, Dict, Any, Optional, Type
from .codex_runtime import CodexRuntime
from .llm_runtime import LLMRuntime
class RuntimeFactory:
"""Factory for creating runtime adapters with auto-detection."""
_RUNTIME_ADAPTERS: List[Type[RuntimeAdapter]] = [CodexRuntime, L... |
class RuntimeFactory:
'''Factory for creating runtime adapters with auto-detection.'''
@classmethod
def get_available_runtimes(cls) -> List[Dict[str, Any]]:
'''Get list of available runtimes on the system.
Returns:
List[Dict[str, Any]]: List of available runtime information
... | 11 | 6 | 21 | 3 | 10 | 7 | 4 | 0.67 | 0 | 7 | 1 | 0 | 0 | 0 | 5 | 5 | 122 | 22 | 61 | 20 | 50 | 41 | 43 | 13 | 37 | 5 | 0 | 4 | 18 |
326,417 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/runtime/llm_runtime.py | apm_cli.runtime.llm_runtime.LLMRuntime | from typing import Dict, Any, Optional
import subprocess
from .base import RuntimeAdapter
class LLMRuntime(RuntimeAdapter):
"""APM adapter for the llm CLI."""
def __init__(self, model_name: Optional[str]=None):
"""Initialize LLM runtime with specified model.
Args:
model_name: Name... |
class LLMRuntime(RuntimeAdapter):
'''APM adapter for the llm CLI.'''
def __init__(self, model_name: Optional[str]=None):
'''Initialize LLM runtime with specified model.
Args:
model_name: Name of the LLM model to use (optional)
'''
pass
def execute_prompt(self, ... | 12 | 8 | 17 | 2 | 10 | 5 | 3 | 0.49 | 1 | 8 | 0 | 0 | 5 | 1 | 8 | 34 | 146 | 25 | 83 | 28 | 71 | 41 | 56 | 22 | 47 | 7 | 5 | 3 | 20 |
326,418 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/runtime/manager.py | apm_cli.runtime.manager.RuntimeManager | import tempfile
import click
from typing import Dict, List, Optional
from colorama import Fore, Style
import sys
import shutil
import subprocess
from pathlib import Path
class RuntimeManager:
"""Manages AI runtime installation and configuration via embedded scripts."""
def __init__(self):
self.runtime... |
class RuntimeManager:
'''Manages AI runtime installation and configuration via embedded scripts.'''
def __init__(self):
pass
def get_embedded_script(self, script_name: str) -> str:
'''Get embedded setup script content.'''
pass
def get_common_script(self) -> str:
'''Ge... | 11 | 10 | 20 | 3 | 15 | 3 | 4 | 0.18 | 0 | 7 | 0 | 0 | 10 | 2 | 10 | 10 | 210 | 38 | 148 | 46 | 136 | 26 | 119 | 40 | 108 | 8 | 0 | 4 | 37 |
326,419 | danielmeppiel/awd-cli | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_awd-cli/src/apm_cli/workflow/parser.py | apm_cli.workflow.parser.WorkflowDefinition | class WorkflowDefinition:
"""Simple container for workflow data."""
def __init__(self, name, file_path, metadata, content):
"""Initialize a workflow definition.
Args:
name (str): Name of the workflow.
file_path (str): Path to the workflow file.
metadata (dic... | class WorkflowDefinition:
'''Simple container for workflow data.'''
def __init__(self, name, file_path, metadata, content):
'''Initialize a workflow definition.
Args:
name (str): Name of the workflow.
file_path (str): Path to the workflow file.
metadata (dict... | 3 | 3 | 14 | 1 | 7 | 7 | 2 | 0.93 | 0 | 0 | 0 | 0 | 2 | 8 | 2 | 2 | 32 | 4 | 15 | 12 | 12 | 14 | 15 | 12 | 12 | 2 | 0 | 1 | 3 |
326,420 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/__init__.py | elicito.Elicit | import joblib
from typing import Any
import tensorflow as tf
from elicito import initialization, losses, networks, optimization, plots, simulations, targets, types, utils
from elicito.types import ExpertDict, Initializer, NFDict, Parallel, Parameter, SaveHist, SaveResults, Target, Trainer
from elicito.elicit import exp... |
class Elicit:
'''
Configure the elicitation method
'''
def __init__(self, model: dict[str, Any], parameters: list[Parameter], targets: list[Target], expert: ExpertDict, trainer: Trainer, optimizer: dict[str, Any], network: NFDict | None=None, initializer: Initializer | None=None):
'''
... | 8 | 8 | 84 | 11 | 46 | 28 | 7 | 0.62 | 0 | 26 | 10 | 0 | 7 | 10 | 7 | 7 | 605 | 82 | 326 | 71 | 297 | 203 | 155 | 49 | 147 | 24 | 0 | 4 | 48 |
326,421 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/elicit.py | elicito.elicit.Dtype | import tensorflow as tf
class Dtype:
"""
Create a tensorflow scalar or array depending on the vtype attribute.
Attributes
----------
vtype
Type of input x.
dim
Dimensionality of input x.
Scalar: `dim = 1`; Vector: `dim > 1`
Returns
-------
:
Tensor... |
class Dtype:
'''
Create a tensorflow scalar or array depending on the vtype attribute.
Attributes
----------
vtype
Type of input x.
dim
Dimensionality of input x.
Scalar: `dim = 1`; Vector: `dim > 1`
Returns
-------
:
Tensor of shape depending on `vty... | 3 | 3 | 20 | 2 | 8 | 10 | 3 | 2.13 | 0 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 59 | 9 | 16 | 6 | 13 | 34 | 14 | 6 | 11 | 5 | 0 | 1 | 6 |
326,422 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/elicit.py | elicito.elicit.Expert | import tensorflow as tf
from elicito.types import ExpertDict, Hyper, Initializer, Parameter, QueriesDict, Target, Trainer, Uniform
from typing import Any, Callable, Optional
class Expert:
"""
specify the expert data
"""
def data(self, dat: dict[str, list[tf.Tensor]]) -> ExpertDict:
"""
... |
class Expert:
'''
specify the expert data
'''
def data(self, dat: dict[str, list[tf.Tensor]]) -> ExpertDict:
'''
Provide elicited-expert data for learning prior distributions.
Parameters
----------
dat
Elicited data from expert provided as dictionary... | 3 | 3 | 51 | 6 | 8 | 37 | 1 | 4.53 | 0 | 6 | 1 | 0 | 2 | 0 | 2 | 2 | 108 | 14 | 17 | 8 | 12 | 77 | 8 | 6 | 5 | 1 | 0 | 0 | 2 |
326,423 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/elicit.py | elicito.elicit.Queries | from elicito.types import ExpertDict, Hyper, Initializer, Parameter, QueriesDict, Target, Trainer, Uniform
from typing import Any, Callable, Optional
class Queries:
"""
specify elicitation techniques
"""
def quantiles(self, quantiles: tuple[float, ...]) -> QueriesDict:
"""
Implement a ... |
class Queries:
'''
specify elicitation techniques
'''
def quantiles(self, quantiles: tuple[float, ...]) -> QueriesDict:
'''
Implement a quantile-based elicitation technique.
Parameters
----------
quantiles
Tuple with respective quantiles ranging betw... | 5 | 5 | 21 | 4 | 6 | 11 | 2 | 2 | 0 | 6 | 1 | 0 | 4 | 0 | 4 | 4 | 91 | 19 | 24 | 12 | 19 | 48 | 18 | 12 | 13 | 3 | 0 | 2 | 6 |
326,424 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/exceptions.py | elicito.exceptions.MissingOptionalDependencyError | class MissingOptionalDependencyError(ImportError):
"""
Raised when an optional dependency is missing
For example, plotting dependencies like matplotlib
"""
def __init__(self, callable_name: str, requirement: str) -> None:
"""
Initialise the error
Parameters
-------... | class MissingOptionalDependencyError(ImportError):
'''
Raised when an optional dependency is missing
For example, plotting dependencies like matplotlib
'''
def __init__(self, callable_name: str, requirement: str) -> None:
'''
Initialise the error
Parameters
---------... | 2 | 2 | 14 | 2 | 3 | 9 | 1 | 3.25 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 13 | 21 | 4 | 4 | 3 | 2 | 13 | 4 | 3 | 2 | 1 | 4 | 0 | 1 |
326,425 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/losses.py | elicito.losses.MMD2 | from typing import Any, Optional, Union
import tensorflow as tf
class MMD2:
"""
Maximum mean discrepancy loss
"""
def __init__(self, kernel: str='energy', **kwargs: dict[Any, Any]):
"""
Compute the biased, squared maximum mean discrepancy
Parameters
----------
... |
class MMD2:
'''
Maximum mean discrepancy loss
'''
def __init__(self, kernel: str='energy', **kwargs: dict[Any, Any]):
'''
Compute the biased, squared maximum mean discrepancy
Parameters
----------
kernel
Kernel type used for computing the MMD.
... | 6 | 6 | 35 | 6 | 12 | 19 | 2 | 1.66 | 0 | 7 | 0 | 0 | 5 | 2 | 5 | 5 | 183 | 35 | 59 | 38 | 39 | 98 | 40 | 24 | 34 | 4 | 0 | 1 | 10 |
326,426 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.ActNorm | import numpy as np
from typing import Any, Callable, Optional
import tensorflow as tf
class ActNorm(tf.keras.Model):
"""Implement an Activation Normalization (ActNorm) Layer.
Activation Normalization is learned invertible normalization,
using a Scale (s) and Bias (b) vector::
y = s * x + b(forward... | null | 6 | 6 | 23 | 2 | 9 | 13 | 2 | 1.95 | 1 | 8 | 0 | 0 | 5 | 2 | 5 | 5 | 146 | 23 | 44 | 19 | 33 | 86 | 28 | 14 | 22 | 3 | 1 | 1 | 9 |
326,427 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.AffineCoupling | import tensorflow as tf
from numpy import pi as PI_CONST
from typing import Any, Callable, Optional
class AffineCoupling(tf.keras.Model):
"""Implement a conditional affine coupling block
Implementation according to [1, 2], with additional
options, such as residual blocks or Monte Carlo Dropout.
[1] ... |
class AffineCoupling(tf.keras.Model):
'''Implement a conditional affine coupling block
Implementation according to [1, 2], with additional
options, such as residual blocks or Monte Carlo Dropout.
[1] Kingma, D. P., & Dhariwal, P. (2018).
Glow: Generative flow with invertible 1x1 convolutions.
... | 5 | 5 | 44 | 4 | 18 | 24 | 3 | 1.52 | 1 | 9 | 1 | 0 | 4 | 4 | 4 | 4 | 193 | 22 | 71 | 38 | 45 | 108 | 31 | 17 | 26 | 4 | 1 | 1 | 10 |
326,428 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.BaseNormal | import tensorflow as tf
from typing import Any, Callable, Optional
class BaseNormal:
"""
standard normal base distribution for normalizing flow
"""
def __call__(self, num_params: int) -> Any:
"""
Multivariate standard normal distribution
distribution has as many dimensions as ... |
class BaseNormal:
'''
standard normal base distribution for normalizing flow
'''
def __call__(self, num_params: int) -> Any:
'''
Multivariate standard normal distribution
distribution has as many dimensions as parameters in the generative model.
Parameters
-----... | 2 | 2 | 21 | 4 | 5 | 12 | 1 | 2.5 | 0 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 26 | 5 | 6 | 3 | 4 | 15 | 4 | 3 | 2 | 1 | 0 | 0 | 1 |
326,429 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.CouplingLayer | import numpy as np
from typing import Any, Callable, Optional
import tensorflow as tf
class CouplingLayer(tf.keras.Model):
"""General wrapper for a coupling layer with different settings."""
def __init__(self, latent_dim: int, coupling_settings: Optional[dict[str, Any]]=None, coupling_design: str | Callable[[... |
class CouplingLayer(tf.keras.Model):
'''General wrapper for a coupling layer with different settings.'''
def __init__(self, latent_dim: int, coupling_settings: Optional[dict[str, Any]]=None, coupling_design: str | Callable[[Any], Any]='affine', permutation: Optional[str]='fixed', use_act_norm: bool=True, act_... | 7 | 7 | 48 | 5 | 19 | 27 | 4 | 1.41 | 1 | 14 | 5 | 0 | 6 | 7 | 6 | 6 | 295 | 34 | 116 | 62 | 77 | 164 | 65 | 30 | 58 | 10 | 1 | 1 | 21 |
326,430 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.DenseCouplingNet | from tensorflow.keras.models import Sequential
import tensorflow as tf
from tensorflow.keras.layers import Dense, Dropout
from typing import Any, Callable, Optional
class DenseCouplingNet(tf.keras.Model):
"""Implement a conditional version of a standard fully connected network.
Would also work as an unconditi... |
class DenseCouplingNet(tf.keras.Model):
'''Implement a conditional version of a standard fully connected network.
Would also work as an unconditional estimator.
'''
def __init__(self, settings: dict[str, Any], dim_out: int, **kwargs: dict[str, Any]):
'''Create a conditional coupling net (FC ne... | 3 | 3 | 55 | 7 | 27 | 25 | 6 | 1 | 1 | 8 | 2 | 0 | 2 | 2 | 2 | 2 | 118 | 18 | 54 | 17 | 44 | 54 | 34 | 10 | 31 | 7 | 1 | 2 | 12 |
326,431 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.InvertibleNetwork | import tensorflow as tf
from typing import Any, Callable, Optional
import numpy as np
class InvertibleNetwork(tf.keras.Model):
"""Implement a chain of conditional invertible coupling layers
Implementation for conditional density estimation.
"""
available_designs = ('affine', 'spline', 'interleaved')
... | null | 9 | 7 | 48 | 5 | 21 | 23 | 3 | 1.08 | 1 | 13 | 1 | 0 | 4 | 7 | 6 | 6 | 300 | 37 | 130 | 64 | 94 | 141 | 63 | 34 | 56 | 6 | 1 | 2 | 19 |
326,432 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.MCDropout | from tensorflow.keras.layers import Dense, Dropout
from typing import Any, Callable, Optional
import tensorflow as tf
class MCDropout(tf.keras.Model):
"""Implement Monte Carlo Dropout
Dropout is implemented as a Bayesian approximation according to [1].
[1] Gal, Y., & Ghahramani, Z. (2016, June). Dropout ... |
class MCDropout(tf.keras.Model):
'''Implement Monte Carlo Dropout
Dropout is implemented as a Bayesian approximation according to [1].
[1] Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian
approximation: Representing model uncertainty in deep learning.
In international conference on mac... | 3 | 3 | 14 | 3 | 3 | 9 | 1 | 3.57 | 1 | 5 | 0 | 0 | 2 | 1 | 2 | 2 | 39 | 9 | 7 | 5 | 4 | 25 | 7 | 5 | 4 | 1 | 1 | 0 | 2 |
326,433 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.MetaDictSetting | from typing import Any, Callable, Optional
class MetaDictSetting:
"""Implement interface for a default meta_dict"""
def __init__(self, meta_dict: dict[str, Any], mandatory_fields: list[str]=[]):
"""Configure meta dict with mandatory arguments
Parameters
----------
meta_dict
... |
class MetaDictSetting:
'''Implement interface for a default meta_dict'''
def __init__(self, meta_dict: dict[str, Any], mandatory_fields: list[str]=[]):
'''Configure meta dict with mandatory arguments
Parameters
----------
meta_dict
Default dictionary.
mandat... | 2 | 2 | 12 | 1 | 3 | 8 | 1 | 2.25 | 0 | 4 | 0 | 0 | 1 | 2 | 1 | 1 | 15 | 2 | 4 | 4 | 2 | 9 | 4 | 4 | 2 | 1 | 0 | 0 | 1 |
326,434 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.Orthogonal | from typing import Any, Callable, Optional
import tensorflow as tf
class Orthogonal(tf.keras.Model):
"""Implement a learnable orthogonal transformation
Implementation according to [1]. Can be used as an alternative
to a fixed ``Permutation`` layer.
[1] Kingma, D. P., & Dhariwal, P. (2018). Glow: Gene... |
class Orthogonal(tf.keras.Model):
'''Implement a learnable orthogonal transformation
Implementation according to [1]. Can be used as an alternative
to a fixed ``Permutation`` layer.
[1] Kingma, D. P., & Dhariwal, P. (2018). Glow: Generative flow
with invertible 1x1 convolutions. Advances in neural ... | 5 | 5 | 15 | 1 | 8 | 7 | 2 | 1.13 | 1 | 4 | 0 | 0 | 4 | 1 | 4 | 4 | 73 | 11 | 31 | 13 | 26 | 35 | 24 | 13 | 19 | 2 | 1 | 1 | 7 |
326,435 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.Permutation | import numpy as np
from typing import Any, Callable, Optional
import tensorflow as tf
class Permutation(tf.keras.Model):
"""Implement a permutation layer
layer to permute the inputs entering a (conditional) coupling layer.
Uses fixed permutations, as these perform equally well compared to
learned perm... |
class Permutation(tf.keras.Model):
'''Implement a permutation layer
layer to permute the inputs entering a (conditional) coupling layer.
Uses fixed permutations, as these perform equally well compared to
learned permutations.
'''
def __init__(self, input_dim: int):
'''Create an inverti... | 5 | 5 | 13 | 1 | 6 | 7 | 1 | 1.23 | 1 | 4 | 0 | 0 | 4 | 2 | 4 | 4 | 62 | 9 | 26 | 9 | 21 | 32 | 15 | 9 | 10 | 2 | 1 | 1 | 5 |
326,436 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.SpectralNormalization | import tensorflow as tf
from typing import Any, Callable, Optional
class SpectralNormalization(tf.keras.layers.Wrapper):
"""Performs spectral normalization on neural network weights.
Adapted from:
https://www.tensorflow.org/addons/api_docs/python/tfa/layers/SpectralNormalization
This wrapper controls... |
class SpectralNormalization(tf.keras.layers.Wrapper):
'''Performs spectral normalization on neural network weights.
Adapted from:
https://www.tensorflow.org/addons/api_docs/python/tfa/layers/SpectralNormalization
This wrapper controls the Lipschitz constant of a layer by
constraining its spectral n... | 6 | 4 | 14 | 1 | 10 | 3 | 2 | 0.5 | 1 | 9 | 0 | 0 | 5 | 5 | 5 | 5 | 88 | 14 | 52 | 19 | 46 | 26 | 36 | 19 | 30 | 3 | 1 | 2 | 10 |
326,437 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/networks.py | elicito.networks.SplineCoupling | import tensorflow as tf
from numpy import e as EULER_CONST
from typing import Any, Callable, Optional
class SplineCoupling(tf.keras.Model):
"""Implement a conditional spline coupling block
Implementation according to [1, 2], with additional
options, such as residual blocks or Monte Carlo Dropout.
[1]... |
class SplineCoupling(tf.keras.Model):
'''Implement a conditional spline coupling block
Implementation according to [1, 2], with additional
options, such as residual blocks or Monte Carlo Dropout.
[1] Durkan, C., Bekasov, A., Murray, I., & Papamakarios, G. (2019).
Neural spline flows. Advances in Ne... | 8 | 8 | 54 | 6 | 25 | 25 | 2 | 1.06 | 1 | 10 | 1 | 0 | 7 | 6 | 7 | 7 | 402 | 53 | 178 | 95 | 147 | 188 | 118 | 72 | 110 | 7 | 1 | 2 | 16 |
326,438 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/simulations.py | elicito.simulations.Priors | from elicito.types import ExpertDict, NFDict, Parameter, Trainer
import tensorflow as tf
from typing import Any, Callable, Optional, Union
class Priors(tf.Module):
"""
Initialize the hyperparameters (i.e., trainable variables)
Parameters
----------
ground_truth
True if expert data are simu... |
class Priors(tf.Module):
'''
Initialize the hyperparameters (i.e., trainable variables)
Parameters
----------
ground_truth
True if expert data are simulated from a given ground truth (oracle)
init_matrix_slice
Samples drawn from the initialization distribution to initialize
... | 3 | 2 | 28 | 2 | 21 | 7 | 2 | 0.88 | 1 | 10 | 4 | 0 | 2 | 7 | 2 | 2 | 89 | 12 | 42 | 20 | 30 | 37 | 16 | 11 | 13 | 2 | 1 | 1 | 3 |
326,439 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.ExpertDict | from typing import Any, TypedDict
class ExpertDict(TypedDict, total=False):
"""
typed dictionary of specification of [`expert`][elicito.elicit.Expert]
"""
ground_truth: dict[str, Any]
num_samples: int
data: dict[str, list[Any]] |
class ExpertDict(TypedDict, total=False):
'''
typed dictionary of specification of [`expert`][elicito.elicit.Expert]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.75 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 1 | 4 | 1 | 3 | 3 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
326,440 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Hyper | from typing import Any, TypedDict
from collections.abc import Callable
import tensorflow as tf
class Hyper(TypedDict):
"""
Typed dictionary for specification of [`hyper`][elicito.elicit.hyper]
"""
name: str
constraint: Callable[[float], tf.Tensor]
constraint_name: str
vtype: Callable[[Any],... |
class Hyper(TypedDict):
'''
Typed dictionary for specification of [`hyper`][elicito.elicit.hyper]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.43 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 1 | 7 | 1 | 6 | 3 | 7 | 1 | 6 | 0 | 1 | 0 | 0 |
326,441 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Initializer | from typing import Any, TypedDict
class Initializer(TypedDict):
"""
typed dictionary for specification of initialization method
"""
method: str | None
distribution: Uniform | None
loss_quantile: float | None
iterations: int | None
hyperparams: dict[str, Any] | None |
class Initializer(TypedDict):
'''
typed dictionary for specification of initialization method
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 1 | 6 | 1 | 5 | 3 | 6 | 1 | 5 | 0 | 1 | 0 | 0 |
326,442 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.NFDict | from collections.abc import Callable
from typing import Any, TypedDict
class NFDict(TypedDict):
"""
Typed dictionary for specification of normalizing flow
See [`network`][elicito.networks.NF]
"""
inference_network: Callable[[Any], Any]
network_specs: dict[str, Any]
base_distribution: Call... |
class NFDict(TypedDict):
'''
Typed dictionary for specification of normalizing flow
See [`network`][elicito.networks.NF]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 3 | 4 | 1 | 3 | 4 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
326,443 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Parallel | from typing import Any, TypedDict
class Parallel(TypedDict):
"""
Typed dictionary for specification of parallelization `parallel`
See [`parallel`][elicito.utils.parallel]
"""
runs: int
cores: int
seeds: list[int] | None |
class Parallel(TypedDict):
'''
Typed dictionary for specification of parallelization `parallel`
See [`parallel`][elicito.utils.parallel]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 2 | 4 | 1 | 3 | 4 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
326,444 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Parameter | from collections.abc import Callable
from typing import Any, TypedDict
class Parameter(dict[str, Any]):
"""Class for specification of a parameter, inheriting from `dict`."""
def __init__(self, name: str, family: Any, hyperparams: dict[str, Hyper] | None, constraint_name: str, constraint: Callable[[float], flo... |
class Parameter(dict[str, Any]):
'''Class for specification of a parameter, inheriting from `dict`.'''
def __init__(self, name: str, family: Any, hyperparams: dict[str, Hyper] | None, constraint_name: str, constraint: Callable[[float], float]):
pass
def __str__(self) -> str:
'''Return a r... | 4 | 3 | 12 | 0 | 11 | 1 | 2 | 0.09 | 1 | 6 | 1 | 0 | 3 | 5 | 3 | 30 | 42 | 4 | 35 | 18 | 24 | 3 | 18 | 11 | 14 | 3 | 2 | 1 | 5 |
326,445 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.QueriesDict | from typing import Any, TypedDict
class QueriesDict(TypedDict, total=False):
"""
Typed dictionary for specification of [`queries`][elicito.elicit.Queries]
"""
name: str
value: Any | None
func_name: str |
class QueriesDict(TypedDict, total=False):
'''
Typed dictionary for specification of [`queries`][elicito.elicit.Queries]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.75 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 1 | 4 | 1 | 3 | 3 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
326,446 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.SaveHist | from typing import Any, TypedDict
class SaveHist(TypedDict):
"""
Typed dictionary for specification of saving `history` results
See [`save_history`][elicito.utils.save_history]
"""
loss: bool
time: bool
loss_component: bool
hyperparameter: bool
hyperparameter_gradient: bool |
class SaveHist(TypedDict):
'''
Typed dictionary for specification of saving `history` results
See [`save_history`][elicito.utils.save_history]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.67 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 2 | 6 | 1 | 5 | 4 | 6 | 1 | 5 | 0 | 1 | 0 | 0 |
326,447 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.SaveResults | from typing import Any, TypedDict
class SaveResults(TypedDict):
"""
Typed dictionary for specification of saving `results`
See [`save_results`][elicito.utils.save_results]
"""
target_quantities: bool
elicited_statistics: bool
prior_samples: bool
model_samples: bool
expert_elicited_... |
class SaveResults(TypedDict):
'''
Typed dictionary for specification of saving `results`
See [`save_results`][elicito.utils.save_results]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 2 | 12 | 1 | 11 | 4 | 12 | 1 | 11 | 0 | 1 | 0 | 0 |
326,448 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Target | from collections.abc import Callable
from typing import Any, TypedDict
class Target(dict[str, Any]):
"""Class for specification of a target, inheriting from `dict`."""
def __init__(self, name: str, query: QueriesDict, target_method: Callable[[Any], Any] | None, loss: Callable[[Any], float], weight: float):
... |
class Target(dict[str, Any]):
'''Class for specification of a target, inheriting from `dict`.'''
def __init__(self, name: str, query: QueriesDict, target_method: Callable[[Any], Any] | None, loss: Callable[[Any], float], weight: float):
pass
def __str__(self) -> str:
'''Return a readable ... | 4 | 3 | 10 | 0 | 9 | 1 | 1 | 0.11 | 1 | 6 | 1 | 0 | 3 | 5 | 3 | 30 | 34 | 3 | 28 | 16 | 17 | 3 | 12 | 9 | 8 | 1 | 2 | 0 | 3 |
326,449 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Trainer | from typing import Any, TypedDict
class Trainer(TypedDict, total=False):
"""
typed dictionary for specification of [`trainer`][elicito.elicit.trainer]
"""
method: str
seed: int
B: int
num_samples: int
epochs: int
seed_chain: int
progress: int |
class Trainer(TypedDict, total=False):
'''
typed dictionary for specification of [`trainer`][elicito.elicit.trainer]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0.38 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 1 | 8 | 1 | 7 | 3 | 8 | 1 | 7 | 0 | 1 | 0 | 0 |
326,450 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/types.py | elicito.types.Uniform | from typing import Any, TypedDict
class Uniform(TypedDict):
"""
typed dictionary for specification of initialization distribution
See [`uniform`][elicito.initialization.uniform]
"""
radius: float | list[float | int]
mean: float | list[float | int]
hyper: list[str] | None |
class Uniform(TypedDict):
'''
typed dictionary for specification of initialization distribution
See [`uniform`][elicito.initialization.uniform]
'''
pass | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 3 | 4 | 1 | 3 | 4 | 4 | 1 | 3 | 0 | 1 | 0 | 0 |
326,451 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/utils.py | elicito.utils.DoubleBound | import tensorflow as tf
class DoubleBound:
"""
constrain double-bounded distributions
"""
def __init__(self, lower: float, upper: float):
"""
Constrain double-bounded distribution
A variable constrained to be in the open interval
(``lower``, ``upper``) is transformed t... |
class DoubleBound:
'''
constrain double-bounded distributions
'''
def __init__(self, lower: float, upper: float):
'''
Constrain double-bounded distribution
A variable constrained to be in the open interval
(``lower``, ``upper``) is transformed to an unconstrained variab... | 6 | 6 | 25 | 6 | 4 | 16 | 1 | 4.15 | 0 | 1 | 0 | 0 | 5 | 2 | 5 | 5 | 136 | 33 | 20 | 12 | 14 | 83 | 20 | 12 | 14 | 1 | 0 | 0 | 5 |
326,452 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/utils.py | elicito.utils.LowerBound | import tensorflow_probability as tfp
from typing import Any, Optional
import tensorflow as tf
class LowerBound:
"""
constrain lower-bounded distributions
"""
def __init__(self, lower: float):
"""
Transform ``lower`` bound variable to unconstrained variable Y
use inverse-softpl... |
class LowerBound:
'''
constrain lower-bounded distributions
'''
def __init__(self, lower: float):
'''
Transform ``lower`` bound variable to unconstrained variable Y
use inverse-softplus transform.
References
----------
- [Stan](https://mc-stan.org/docs/r... | 4 | 4 | 22 | 5 | 3 | 14 | 1 | 4 | 0 | 2 | 0 | 0 | 3 | 1 | 3 | 3 | 72 | 17 | 11 | 7 | 7 | 44 | 11 | 7 | 7 | 1 | 0 | 0 | 3 |
326,453 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/src/elicito/utils.py | elicito.utils.UpperBound | from typing import Any, Optional
import tensorflow as tf
import tensorflow_probability as tfp
class UpperBound:
"""
transform ``upper`` bounded distribution
"""
def __init__(self, upper: float):
"""
Transform ``upper`` bounded x into unconstrained y
use inverse-softplus transf... |
class UpperBound:
'''
transform ``upper`` bounded distribution
'''
def __init__(self, upper: float):
'''
Transform ``upper`` bounded x into unconstrained y
use inverse-softplus transform.
Parameters
----------
upper
Upper bound of variable X.... | 4 | 4 | 23 | 5 | 3 | 14 | 1 | 4.18 | 0 | 2 | 0 | 0 | 3 | 1 | 3 | 3 | 76 | 19 | 11 | 7 | 7 | 46 | 11 | 7 | 7 | 1 | 0 | 0 | 3 |
326,454 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/docs/gen_doc_stubs.py | gen_doc_stubs.PackageInfo | from attrs import define
@define
class PackageInfo:
"""
Package information used to help us auto-generate the docs
Not stricly needed anymore now that mkdocstrings-python has a summary option,
but being kept in case we need something like this pattern again.
"""
full_name: str
stem: str
... | @define
class PackageInfo:
'''
Package information used to help us auto-generate the docs
Not stricly needed anymore now that mkdocstrings-python has a summary option,
but being kept in case we need something like this pattern again.
'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 2 | 4 | 1 | 3 | 5 | 4 | 1 | 3 | 0 | 0 | 0 | 0 |
326,455 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/docs/tutorials/getting-started-deep.py | getting-started-deep.ToyModel | import tensorflow as tf
from typing import Any
class ToyModel:
"""
generative model
"""
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
"""
Compute target quantities from generative model
Parameters
----------
prior_samples
... |
class ToyModel:
'''
generative model
'''
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
'''
Compute target quantities from generative model
Parameters
----------
prior_samples
prior samples
design_matrix
... | 2 | 2 | 31 | 6 | 8 | 17 | 1 | 2.22 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 36 | 7 | 9 | 6 | 7 | 20 | 7 | 6 | 5 | 1 | 0 | 0 | 1 |
326,456 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/docs/tutorials/getting-started-param.py | getting-started-param.ToyModel | from typing import Any
import tensorflow as tf
class ToyModel:
"""
generative model
"""
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
"""
Compute target quantities from generative model
Parameters
----------
prior_samples
... |
class ToyModel:
'''
generative model
'''
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
'''
Compute target quantities from generative model
Parameters
----------
prior_samples
prior samples
design_matrix
... | 2 | 2 | 31 | 6 | 8 | 17 | 1 | 2.22 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 36 | 7 | 9 | 6 | 7 | 20 | 7 | 6 | 5 | 1 | 0 | 0 | 1 |
326,457 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/scripts/print-conda-recipe-pins.py | print-conda-recipe-pins.VersionInfoHere | from attrs import define
@define
class VersionInfoHere:
"""Version info class for use in this script"""
name: str
min_pin: str
max_pin: str | @define
class VersionInfoHere:
'''Version info class for use in this script'''
pass | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 1 | 4 | 1 | 3 | 1 | 4 | 1 | 3 | 0 | 0 | 0 | 0 |
326,458 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/docs/how-to-guides/save-and-load-eliobj.py | save-and-load-eliobj.ToyModel | from typing import Any, Union
import tensorflow as tf
class ToyModel:
"""
Generative model
"""
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
"""
Run the generative model
Parameters
----------
prior_samples
samples fro... |
class ToyModel:
'''
Generative model
'''
def __call__(self, prior_samples: Any, design_matrix: Any) -> dict[str, Any]:
'''
Run the generative model
Parameters
----------
prior_samples
samples from the prior distribution
design_matrix
... | 2 | 2 | 38 | 6 | 14 | 18 | 1 | 1.4 | 0 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 43 | 7 | 15 | 9 | 13 | 21 | 10 | 9 | 8 | 1 | 0 | 0 | 1 |
326,459 | florence-bockting/elicito | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/florence-bockting_elicito/docs/how-to-guides/use-discrete-rv.py | use-discrete-rv.ToyModel | import elicito as el
from typing import Any
import tensorflow as tf
class ToyModel:
"""
generative model with discrete likelihood
"""
def __call__(self, prior_samples: Any, design_matrix: Any, total_count: int, temp: float) -> dict[str, Any]:
"""
Sample from the generative model
... |
class ToyModel:
'''
generative model with discrete likelihood
'''
def __call__(self, prior_samples: Any, design_matrix: Any, total_count: int, temp: float) -> dict[str, Any]:
'''
Sample from the generative model
Parameters
----------
prior_samples
sa... | 2 | 2 | 47 | 9 | 16 | 22 | 1 | 1.47 | 0 | 5 | 0 | 0 | 1 | 0 | 1 | 1 | 52 | 10 | 17 | 11 | 13 | 25 | 10 | 9 | 8 | 1 | 0 | 0 | 1 |
326,460 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/datasets/base_dataset.py | base_miner.datasets.base_dataset.BaseDataset | from torchvision.transforms import Compose
from base_miner.datasets.download_data import load_huggingface_dataset
from typing import Optional
from abc import ABC, abstractmethod
from datasets import Dataset
class BaseDataset(ABC):
def __init__(self, huggingface_dataset_path: Optional[str]=None, huggingface_datase... |
class BaseDataset(ABC):
def __init__(self, huggingface_dataset_path: Optional[str]=None, huggingface_dataset_split: str='train', huggingface_dataset_name: Optional[str]=None, huggingface_dataset: Optional[Dataset]=None, download_mode: Optional[str]=None, transforms: Optional[Compose]=None):
'''Base class ... | 6 | 3 | 21 | 2 | 11 | 8 | 2 | 0.62 | 1 | 7 | 0 | 1 | 3 | 6 | 3 | 23 | 69 | 9 | 37 | 20 | 23 | 23 | 24 | 10 | 20 | 4 | 4 | 2 | 6 |
326,461 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/datasets/image_dataset.py | base_miner.datasets.image_dataset.ImageDataset | from torchvision.transforms import Compose
from typing import Optional
from io import BytesIO
from datasets import Dataset
from .download_data import download_image
from PIL import Image
from .base_dataset import BaseDataset
class ImageDataset(BaseDataset):
def __init__(self, huggingface_dataset_path: Optional[st... |
class ImageDataset(BaseDataset):
def __init__(self, huggingface_dataset_path: Optional[str]=None, huggingface_dataset_split: str='train', huggingface_dataset_name: Optional[str]=None, huggingface_dataset: Optional[Dataset]=None, download_mode: Optional[str]=None, transforms: Optional[Compose]=None):
'''In... | 4 | 3 | 32 | 4 | 16 | 12 | 4 | 0.75 | 1 | 7 | 0 | 0 | 3 | 0 | 3 | 26 | 98 | 14 | 48 | 15 | 36 | 36 | 27 | 7 | 23 | 11 | 5 | 2 | 13 |
326,462 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/datasets/real_fake_dataset.py | base_miner.datasets.real_fake_dataset.RealFakeDataset | import torch
import numpy as np
class RealFakeDataset:
def __init__(self, real_image_datasets: list, fake_image_datasets: list, fake_prob=0.5, source_label_mapping=None):
"""
Initialize the RealFakeDataset instance.
Args:
real_image_datasets (list): List of ImageDataset object... |
class RealFakeDataset:
def __init__(self, real_image_datasets: list, fake_image_datasets: list, fake_prob=0.5, source_label_mapping=None):
'''
Initialize the RealFakeDataset instance.
Args:
real_image_datasets (list): List of ImageDataset objects containing real images
... | 7 | 3 | 17 | 2 | 9 | 6 | 2 | 0.6 | 0 | 4 | 0 | 0 | 4 | 5 | 5 | 5 | 90 | 16 | 47 | 20 | 40 | 28 | 37 | 19 | 31 | 4 | 0 | 1 | 8 |
326,463 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/detectors/feature_detector.py | base_miner.detectors.feature_detector.FeatureDetector | import torch
from abc import ABC, abstractmethod
from pathlib import Path
import yaml
from typing import Optional
from base_miner.detectors.configs.constants import CONFIGS_DIR
import bittensor as bt
from huggingface_hub import hf_hub_download
from PIL import Image
class FeatureDetector(ABC):
"""Abstract base clas... |
class FeatureDetector(ABC):
'''Abstract base class for detecting image features via binary classification.
This class is intended to be subclassed by detector implementations
using different underlying model architectures, routing via gates, or
configurations.
Attributes:
model_name (str): ... | 10 | 7 | 15 | 2 | 7 | 5 | 2 | 0.94 | 1 | 4 | 0 | 2 | 7 | 5 | 7 | 27 | 126 | 25 | 52 | 23 | 42 | 49 | 48 | 17 | 40 | 4 | 4 | 3 | 16 |
326,464 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/detectors/roadwork_detector.py | base_miner.detectors.roadwork_detector.RoadworkDetector | from PIL import Image
from base_miner.gating_mechanisms import GatingMechanism
from base_miner.registry import DETECTOR_REGISTRY
from base_miner.detectors import FeatureDetector
@DETECTOR_REGISTRY.register_module(module_name='ROADWORK')
class RoadworkDetector(FeatureDetector):
"""
This DeepfakeDetector subclas... | @DETECTOR_REGISTRY.register_module(module_name='ROADWORK')
class RoadworkDetector(FeatureDetector):
'''
This DeepfakeDetector subclass implements Content-Aware Model Orchestration
(CAMO), a mixture-of-experts approach to the binary classification of
real and fake images, breaking the classification prob... | 5 | 4 | 15 | 1 | 7 | 7 | 2 | 1.71 | 1 | 5 | 1 | 0 | 3 | 2 | 3 | 30 | 66 | 9 | 21 | 11 | 17 | 36 | 16 | 11 | 12 | 3 | 5 | 2 | 5 |
326,465 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/detectors/vit_detector.py | base_miner.detectors.vit_detector.ViTImageDetector | import torch
import torchvision.transforms as transforms
from base_miner.detectors import FeatureDetector
import random
from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
import bittensor as bt
from PIL import Image
from base_miner.registry import DETECTOR_REGISTRY
import gc
@DETECT... | @DETECTOR_REGISTRY.register_module(module_name='ViT')
class ViTImageDetector(FeatureDetector):
'''
ViTImageDetector subclass that initializes a pretrained model
for binary classification of roadwork.
Attributes:
model_name (str): Name of the detector instance.
config_name (str): Name of ... | 10 | 4 | 10 | 1 | 7 | 2 | 2 | 0.41 | 1 | 3 | 0 | 0 | 8 | 4 | 8 | 35 | 97 | 16 | 59 | 20 | 50 | 24 | 48 | 19 | 39 | 5 | 5 | 1 | 15 |
326,466 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/gating_mechanisms/gate.py | base_miner.gating_mechanisms.gate.Gate | import numpy as np
from abc import ABC, abstractmethod
from PIL import Image
class Gate(ABC):
"""
Abstract base class for image content detection and preprocessing.
Used to route deepfake detection inference inputs to tailored models
in a single agent or mixture-of-experts design.
This class is in... |
class Gate(ABC):
'''
Abstract base class for image content detection and preprocessing.
Used to route deepfake detection inference inputs to tailored models
in a single agent or mixture-of-experts design.
This class is intended to be subclassed by specific gate
implementations that handle diffe... | 6 | 3 | 5 | 0 | 2 | 2 | 1 | 1.7 | 1 | 1 | 0 | 0 | 3 | 2 | 3 | 23 | 33 | 6 | 10 | 8 | 4 | 17 | 8 | 6 | 4 | 1 | 4 | 0 | 3 |
326,467 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/gating_mechanisms/gating_mechanism.py | base_miner.gating_mechanisms.gating_mechanism.GatingMechanism | from base_miner.registry import GATE_REGISTRY
from PIL import Image
class GatingMechanism:
"""
This class orchestrates multi-gate content detection and content-specific
preprocessing to facilitate use by downstream models
This is useful for routing images to appropriate detectors
trained to handle... |
class GatingMechanism:
'''
This class orchestrates multi-gate content detection and content-specific
preprocessing to facilitate use by downstream models
This is useful for routing images to appropriate detectors
trained to handle different content types in a mixture-of-experts
framework such a... | 3 | 1 | 5 | 1 | 5 | 0 | 2 | 0.7 | 0 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 21 | 4 | 10 | 7 | 7 | 7 | 10 | 7 | 7 | 3 | 0 | 2 | 4 |
326,468 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/gating_mechanisms/roadwork_gate.py | base_miner.gating_mechanisms.roadwork_gate.RoadworkGate | from PIL import Image
from base_miner.gating_mechanisms import Gate
import numpy as np
from base_miner.registry import GATE_REGISTRY
@GATE_REGISTRY.register_module(module_name='ROADWORK')
class RoadworkGate(Gate):
"""
Gate subclass for roadwork content detection and preprocessing.
Attributes:
gate... | @GATE_REGISTRY.register_module(module_name='ROADWORK')
class RoadworkGate(Gate):
'''
Gate subclass for roadwork content detection and preprocessing.
Attributes:
gate_name (str): The name of the gate.
predictor_path (str): Path to dlib face landmark model.
'''
def __init__(self, gate... | 5 | 3 | 11 | 2 | 4 | 5 | 1 | 1.83 | 1 | 3 | 0 | 0 | 3 | 0 | 3 | 3 | 44 | 10 | 12 | 7 | 8 | 22 | 12 | 7 | 8 | 2 | 1 | 1 | 4 |
326,469 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/base_miner/registry.py | base_miner.registry.Registry | class Registry(object):
def __init__(self):
self.data = {}
def register_module(self, module_name=None):
def _register(cls):
name = module_name
if module_name is None:
name = cls.__name__
self.data[name] = cls
return cls
r... | class Registry(object):
def __init__(self):
pass
def register_module(self, module_name=None):
pass
def _register(cls):
pass
def __getitem__(self, key):
pass
def __contains__(self, key):
pass | 6 | 0 | 4 | 0 | 4 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 4 | 1 | 4 | 4 | 19 | 4 | 15 | 8 | 9 | 0 | 15 | 8 | 9 | 2 | 1 | 1 | 6 |
326,470 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.ApplyDeeperForensicsDistortion | import random
import numpy as np
import torch
class ApplyDeeperForensicsDistortion:
"""Wrapper for applying DeeperForensics distortions."""
def __init__(self, distortion_type, level_min=0, level_max=3):
self.distortion_type = distortion_type
self.level_min = level_min
self.level_max = ... |
class ApplyDeeperForensicsDistortion:
'''Wrapper for applying DeeperForensics distortions.'''
def __init__(self, distortion_type, level_min=0, level_max=3):
pass
def __call__(self, img, level=None):
pass | 3 | 1 | 17 | 3 | 14 | 0 | 4 | 0.04 | 0 | 0 | 0 | 0 | 2 | 6 | 2 | 2 | 37 | 8 | 28 | 9 | 25 | 1 | 26 | 9 | 23 | 6 | 0 | 1 | 7 |
326,471 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.CLAHE | import cv2
from PIL import Image
import numpy as np
class CLAHE:
"""Contrast Limited Adaptive Histogram Equalization."""
def __init__(self):
self.clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))
def __call__(self, image):
image_np = np.array(image)
if len(image_np.shape... |
class CLAHE:
'''Contrast Limited Adaptive Histogram Equalization.'''
def __init__(self):
pass
def __call__(self, image):
pass | 3 | 1 | 9 | 2 | 6 | 3 | 2 | 0.46 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 22 | 5 | 13 | 9 | 10 | 6 | 12 | 9 | 9 | 2 | 0 | 1 | 3 |
326,472 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.ComposeWithParams | import torchvision.transforms as transforms
class ComposeWithParams:
def __init__(self, transforms):
self.transforms = transforms
self.params = {}
def __call__(self, input_data, clear_params=True):
if clear_params:
self.params = {}
output_data = []
list_inp... |
class ComposeWithParams:
def __init__(self, transforms):
pass
def __call__(self, input_data, clear_params=True):
pass | 3 | 0 | 16 | 2 | 14 | 0 | 5 | 0 | 0 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 33 | 5 | 28 | 10 | 25 | 0 | 27 | 10 | 24 | 9 | 0 | 4 | 10 |
326,473 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.ConvertToRGB | class ConvertToRGB:
def __call__(self, img):
img = img.convert('RGB')
return img | class ConvertToRGB:
def __call__(self, img):
pass | 2 | 0 | 3 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 0 | 4 | 2 | 2 | 0 | 4 | 2 | 2 | 1 | 0 | 0 | 1 |
326,474 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.RandomHorizontalFlipWithParams | import torchvision.transforms as transforms
import torch
class RandomHorizontalFlipWithParams(transforms.RandomHorizontalFlip):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.params = {}
def forward(self, img, do_flip=None):
if do_flip is not None:
... |
class RandomHorizontalFlipWithParams(transforms.RandomHorizontalFlip):
def __init__(self, *args, **kwargs):
pass
def forward(self, img, do_flip=None):
pass | 3 | 0 | 7 | 0 | 7 | 0 | 4 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 16 | 2 | 14 | 4 | 11 | 0 | 12 | 4 | 9 | 6 | 1 | 1 | 7 |
326,475 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.RandomResizedCropWithParams | import torchvision.transforms.functional as F
import torchvision.transforms as transforms
class RandomResizedCropWithParams(transforms.RandomResizedCrop):
def __init__(self, *args, include_point=None, **kwargs):
super().__init__(*args, **kwargs)
self.params = None
self.include_point = incl... |
class RandomResizedCropWithParams(transforms.RandomResizedCrop):
def __init__(self, *args, include_point=None, **kwargs):
pass
def forward(self, img, crop_params=None):
'''
Args:
img: PIL Image to be cropped and resized
crop_params: Optional pre-computed crop p... | 3 | 1 | 16 | 2 | 12 | 3 | 4 | 0.25 | 1 | 1 | 0 | 0 | 2 | 4 | 2 | 2 | 34 | 4 | 24 | 9 | 21 | 6 | 21 | 8 | 18 | 7 | 1 | 3 | 8 |
326,476 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.RandomRotationWithParams | import torchvision.transforms as transforms
class RandomRotationWithParams(transforms.RandomRotation):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.params = None
def forward(self, img, angle=None):
if angle is None:
angle = self.get_params(se... |
class RandomRotationWithParams(transforms.RandomRotation):
def __init__(self, *args, **kwargs):
pass
def forward(self, img, angle=None):
pass | 3 | 0 | 4 | 0 | 4 | 0 | 2 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 10 | 1 | 9 | 4 | 6 | 0 | 9 | 4 | 6 | 2 | 1 | 1 | 3 |
326,477 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.RandomVerticalFlipWithParams | import torchvision.transforms as transforms
import torch
class RandomVerticalFlipWithParams(transforms.RandomVerticalFlip):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.params = {}
def forward(self, img, do_flip=None):
if do_flip is not None:
... |
class RandomVerticalFlipWithParams(transforms.RandomVerticalFlip):
def __init__(self, *args, **kwargs):
pass
def forward(self, img, do_flip=None):
pass | 3 | 0 | 7 | 0 | 7 | 0 | 4 | 0 | 1 | 1 | 0 | 0 | 2 | 1 | 2 | 2 | 15 | 1 | 14 | 4 | 11 | 0 | 12 | 4 | 9 | 6 | 1 | 1 | 7 |
326,478 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/image_transforms.py | image_transforms.TensorCLAHE | import cv2
import numpy as np
import torch
class TensorCLAHE:
def __init__(self):
self.clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))
def __call__(self, tensor):
img_np = tensor.permute(1, 2, 0).numpy() * 255
img_np = img_np.astype(np.uint8)
channels = cv2.split(i... |
class TensorCLAHE:
def __init__(self):
pass
def __call__(self, tensor):
pass | 3 | 0 | 8 | 1 | 5 | 2 | 1 | 0.27 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | 2 | 17 | 3 | 11 | 8 | 8 | 3 | 11 | 8 | 8 | 1 | 0 | 0 | 2 |
326,479 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/mock.py | mock.MockDendrite | from typing import List
import random
import time
import asyncio
import numpy as np
import bittensor as bt
class MockDendrite(bt.dendrite):
"""
Replaces a real bittensor network request with a mock request that just returns some static response
for all axons that are passed and adds some random delay.
... |
class MockDendrite(bt.dendrite):
'''
Replaces a real bittensor network request with a mock request that just returns some static response
for all axons that are passed and adds some random delay.
'''
def __init__(self, wallet):
pass
async def forward(self, axons: List[bt.axon], synaps... | 6 | 4 | 24 | 3 | 16 | 5 | 2 | 0.41 | 1 | 6 | 0 | 0 | 3 | 0 | 3 | 3 | 65 | 10 | 39 | 17 | 25 | 16 | 29 | 9 | 23 | 3 | 1 | 1 | 8 |
326,480 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/mock.py | mock.MockImageDataset | class MockImageDataset:
def __init__(self, huggingface_dataset_path: str, huggingface_datset_split: str='train', huggingface_datset_name: str=None, create_splits: bool=False, download_mode: str=None):
self.huggingface_dataset_path = huggingface_dataset_path
self.huggingface_dataset_name = huggingfa... | class MockImageDataset:
def __init__(self, huggingface_dataset_path: str, huggingface_datset_split: str='train', huggingface_datset_name: str=None, create_splits: bool=False, download_mode: str=None):
pass
def __getitem__(self, index: int) -> dict:
pass
def __len__(self):
pass
... | 5 | 0 | 5 | 0 | 5 | 0 | 1 | 0.05 | 0 | 5 | 0 | 0 | 4 | 4 | 4 | 4 | 23 | 4 | 19 | 16 | 7 | 1 | 12 | 9 | 7 | 1 | 0 | 0 | 4 |
326,481 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/mock.py | mock.MockMetagraph | import bittensor as bt
class MockMetagraph(bt.metagraph):
def __init__(self, netuid, network='mock', subtensor=None):
super().__init__(netuid=netuid, network=network, sync=False)
self.default_ip = '127.0.0.0'
self.default_port = 8092
if subtensor is not None:
self.subte... |
class MockMetagraph(bt.metagraph):
def __init__(self, netuid, network='mock', subtensor=None):
pass | 2 | 0 | 15 | 3 | 12 | 0 | 3 | 0 | 1 | 1 | 0 | 0 | 1 | 3 | 1 | 1 | 16 | 3 | 13 | 6 | 11 | 0 | 13 | 6 | 11 | 3 | 1 | 1 | 3 |
326,482 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/mock.py | mock.MockSubtensor | import bittensor as bt
class MockSubtensor(bt.MockSubtensor):
def __init__(self, netuid, n=16, wallet=None, network='mock'):
super().__init__(network=network)
bt.MockSubtensor.reset()
if not self.subnet_exists(netuid):
self.create_subnet(netuid)
if wallet is not None:
... |
class MockSubtensor(bt.MockSubtensor):
def __init__(self, netuid, n=16, wallet=None, network='mock'):
pass | 2 | 0 | 32 | 3 | 27 | 3 | 6 | 0.11 | 1 | 3 | 0 | 0 | 1 | 0 | 1 | 1 | 33 | 3 | 28 | 4 | 26 | 3 | 16 | 3 | 14 | 6 | 1 | 2 | 6 |
326,483 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/utils/mock.py | mock.MockSyntheticDataGenerator | import numpy as np
from natix.validator.config import MODEL_NAMES
class MockSyntheticDataGenerator:
def __init__(self, prompt_type, use_random_t2v_model, t2v_model_name):
self.prompt_type = prompt_type
self.t2v_model_name = t2v_model_name
self.use_random_t2v_model = use_random_t2v_model
... |
class MockSyntheticDataGenerator:
def __init__(self, prompt_type, use_random_t2v_model, t2v_model_name):
pass
def generate(self, k=1, real_images=None, modality='image'):
pass
def load_diffuser(self, t2v_model_name) -> None:
'''
loads a huggingface diffuser model.
... | 4 | 1 | 6 | 0 | 5 | 1 | 2 | 0.2 | 0 | 1 | 0 | 0 | 3 | 3 | 3 | 3 | 21 | 3 | 15 | 7 | 11 | 3 | 14 | 7 | 10 | 2 | 0 | 1 | 5 |
326,484 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/base/miner.py | natix.base.miner.BaseMinerNeuron | import bittensor as bt
import time
import asyncio
from natix.base.neuron import BaseNeuron
import threading
import argparse
from typing import Union
from natix.utils.config import add_miner_args
import typing
import traceback
class BaseMinerNeuron(BaseNeuron):
"""
Base class for Bittensor miners.
"""
n... |
class BaseMinerNeuron(BaseNeuron):
'''
Base class for Bittensor miners.
'''
@classmethod
def add_args(cls, parser: argparse.ArgumentParser):
pass
def __init__(self, config=None):
pass
def run(self):
'''
Initiates and manages the main loop for the miner ... | 12 | 9 | 22 | 4 | 9 | 10 | 3 | 1.14 | 1 | 8 | 0 | 1 | 9 | 6 | 10 | 42 | 232 | 46 | 88 | 21 | 76 | 100 | 83 | 20 | 72 | 6 | 5 | 4 | 26 |
326,485 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/base/neuron.py | natix.base.neuron.BaseNeuron | from natix.utils.mock import MockMetagraph, MockSubtensor
from natix.utils.misc import ttl_get_block
import bittensor as bt
from natix import __spec_version__ as spec_version
from natix.utils.config import add_args, check_config, config
import copy
from abc import ABC, abstractmethod
class BaseNeuron(ABC):
"""
... |
class BaseNeuron(ABC):
'''
Base class for Bittensor miners. This class is abstract and should be inherited by a subclass.
It contains the core logic for all neurons; validators and miners.
In addition to creating a wallet, subtensor, and metagraph, this class also handles the synchronization
of the... | 18 | 3 | 9 | 1 | 6 | 2 | 2 | 0.33 | 1 | 3 | 2 | 2 | 9 | 3 | 12 | 32 | 137 | 28 | 83 | 24 | 66 | 27 | 62 | 19 | 49 | 3 | 4 | 1 | 18 |
326,486 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/base/validator.py | natix.base.validator.BaseValidatorNeuron | import threading
import asyncio
from natix.utils.mock import MockDendrite
import time
import argparse
import copy
from traceback import print_exception
from natix.utils.config import add_validator_args
import numpy as np
from natix.validator.miner_performance_tracker import MinerPerformanceTracker
from natix.base.utils... |
class BaseValidatorNeuron(BaseNeuron):
'''
Base class for Bittensor validators. Your validator should inherit from this class.
'''
@classmethod
def add_args(cls, parser: argparse.ArgumentParser):
pass
def __init__(self, config=None):
pass
def serve_axon(self):
... | 22 | 13 | 24 | 3 | 16 | 5 | 3 | 0.36 | 1 | 14 | 2 | 1 | 17 | 14 | 18 | 50 | 466 | 70 | 296 | 70 | 272 | 106 | 236 | 58 | 214 | 9 | 5 | 3 | 55 |
326,487 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/protocol.py | natix.protocol.ExtendedImageSynapse | class ExtendedImageSynapse(ImageSynapse):
model_url: str = '' | class ExtendedImageSynapse(ImageSynapse):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 2 | 1 | 0 | 2 | 2 | 1 | 0 | 2 | 0 | 0 |
326,488 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/protocol.py | natix.protocol.ImageSynapse | import pydantic
import bittensor as bt
class ImageSynapse(bt.Synapse):
"""
This protocol helps in handling image/prediction request and response communication between
the miner and the validator.
Attributes:
- image: a bas64 encoded images
- prediction: a float indicating the probabilty that ... |
class ImageSynapse(bt.Synapse):
'''
This protocol helps in handling image/prediction request and response communication between
the miner and the validator.
Attributes:
- image: a bas64 encoded images
- prediction: a float indicating the probabilty that the image is AI generated/modified.
... | 2 | 2 | 10 | 1 | 2 | 7 | 1 | 1.64 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 34 | 6 | 11 | 5 | 9 | 18 | 6 | 5 | 4 | 1 | 1 | 0 | 1 |
326,489 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/synthetic_data_generation/prompt_generator.py | natix.synthetic_data_generation.prompt_generator.PromptGenerator | from transformers import pipeline
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Blip2ForConditionalGeneration, Blip2Processor
import bittensor as bt
from transformers import logging as transformers_logging
from natix.validator.config import HUGGINGFACE_CACHE_DIR
from PIL import Image
import... |
class PromptGenerator:
'''
A class for generating and moderating image annotations using transformer models.
This class provides functionality to generate descriptive captions for images
using BLIP2 models and optionally moderate the generated text using a separate
language model.
'''
def ... | 7 | 6 | 30 | 4 | 19 | 8 | 4 | 0.46 | 0 | 6 | 0 | 0 | 6 | 6 | 6 | 6 | 194 | 32 | 112 | 32 | 100 | 51 | 86 | 26 | 79 | 9 | 0 | 3 | 26 |
326,490 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/synthetic_data_generation/synthetic_data_generator.py | natix.synthetic_data_generation.synthetic_data_generator.SyntheticDataGenerator | from PIL import Image
import numpy as np
from natix.validator.cache import ImageCache
from typing import Any, Dict, Optional, Union
from diffusers.utils import export_to_video
from natix.synthetic_data_generation.image_utils import create_random_mask
from natix.synthetic_data_generation.prompt_utils import truncate_pro... |
class SyntheticDataGenerator:
'''
A class for generating synthetic images and videos based on text prompts.
This class supports different prompt generation strategies and can utilize
various text-to-video (t2v) and text-to-image (t2i) models.
Attributes:
use_random_model: Whether to randoml... | 9 | 9 | 45 | 6 | 29 | 10 | 6 | 0.38 | 0 | 16 | 2 | 0 | 8 | 8 | 8 | 8 | 384 | 60 | 236 | 81 | 209 | 89 | 179 | 61 | 170 | 14 | 0 | 3 | 47 |
326,491 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/cache/base_cache.py | natix.validator.cache.base_cache.BaseCache | from .util import get_most_recent_update_time, seconds_to_str
from abc import ABC, abstractmethod
import bittensor as bt
from .download import download_files, list_hf_files
import time
import numpy as np
from typing import Any, Dict, List, Optional, Union
import asyncio
from pathlib import Path
class BaseCache(ABC):
... |
class BaseCache(ABC):
'''
Abstract base class for managing file caches with compressed sources.
This class provides the basic infrastructure for maintaining both a compressed
source cache and an extracted cache, with automatic refresh intervals and
background update tasks.
'''
def __init__... | 20 | 17 | 13 | 1 | 10 | 2 | 2 | 0.22 | 1 | 10 | 0 | 1 | 16 | 13 | 16 | 36 | 236 | 38 | 162 | 69 | 131 | 36 | 138 | 52 | 121 | 5 | 4 | 3 | 38 |
326,492 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/cache/image_cache.py | natix.validator.cache.image_cache.ImageCache | from pathlib import Path
from PIL import Image
import random
from .base_cache import BaseCache
from .util import is_parquet_complete
from typing import Any, Dict, List, Optional, Union
from .extract import extract_images_from_parquet
import json
import os
import bittensor as bt
class ImageCache(BaseCache):
"""
... |
class ImageCache(BaseCache):
'''
A class to manage image caching from parquet files.
This class handles the caching, updating, and sampling of images stored
in parquet files. It maintains both a compressed cache of parquet files
and an extracted cache of images ready for processing.
'''
de... | 5 | 5 | 30 | 3 | 21 | 7 | 5 | 0.4 | 1 | 7 | 0 | 0 | 4 | 1 | 4 | 40 | 133 | 17 | 83 | 29 | 68 | 33 | 56 | 16 | 51 | 9 | 5 | 4 | 19 |
326,493 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/cache/util.py | natix.validator.cache.util.FileType | from enum import Enum, auto
class FileType(Enum):
PARQUET = auto()
ZIP = auto() |
class FileType(Enum):
pass | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 3 | 0 | 3 | 3 | 2 | 0 | 3 | 3 | 2 | 0 | 4 | 0 | 0 |
326,494 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/miner_performance_tracker.py | natix.validator.miner_performance_tracker.MinerPerformanceTracker | import bittensor as bt
from typing import Dict
from collections import deque
import numpy as np
from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef, precision_score, recall_score, roc_auc_score
class MinerPerformanceTracker:
"""
Tracks all recent miner performance to facilitate reward compu... |
class MinerPerformanceTracker:
'''
Tracks all recent miner performance to facilitate reward computation.
'''
def __init__(self, store_last_n_predictions: int=100):
pass
def reset_miner_history(self, uid: int, miner_hotkey: str):
'''
Reset the history for a miner.
'... | 7 | 6 | 15 | 2 | 8 | 5 | 2 | 0.62 | 0 | 5 | 0 | 0 | 6 | 4 | 6 | 6 | 98 | 17 | 50 | 24 | 43 | 31 | 49 | 23 | 42 | 7 | 0 | 2 | 14 |
326,495 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/organic_task_distributor.py | natix.validator.organic_task_distributor.OrganicTaskDistributor | import hashlib
import bittensor as bt
import random
from collections import defaultdict, deque
import time
import asyncio
from typing import Dict, List, Optional, Set, Tuple
from natix.utils.uids import get_random_uids
class OrganicTaskDistributor:
"""
Handles organic task distribution with anti-collusion mech... |
class OrganicTaskDistributor:
'''
Handles organic task distribution with anti-collusion mechanisms.
This class is designed to be used within an async context and handles
its own dendrite initialization to avoid context issues.
'''
def __init__(self, validator, miners_per_task: int=3, deduplica... | 12 | 10 | 29 | 5 | 23 | 2 | 3 | 0.11 | 0 | 9 | 1 | 0 | 10 | 12 | 10 | 10 | 310 | 57 | 227 | 78 | 200 | 26 | 123 | 58 | 111 | 7 | 0 | 3 | 31 |
326,496 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/natix/validator/proxy.py | natix.validator.proxy.ProxyCounter | from datetime import date
import json
import os
class ProxyCounter:
def __init__(self, save_path):
self.save_path = save_path
if os.path.exists(save_path):
try:
self.proxy_logs = json.load(open(save_path))
except Exception as e:
print(f'Error... |
class ProxyCounter:
def __init__(self, save_path):
pass
def update(self, is_success):
pass
def save(self):
pass | 4 | 0 | 6 | 0 | 6 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 3 | 2 | 3 | 3 | 22 | 2 | 20 | 8 | 16 | 0 | 18 | 7 | 14 | 3 | 0 | 2 | 6 |
326,497 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/neurons/miner.py | neurons.miner.Miner | from base_miner.registry import DETECTOR_REGISTRY
from natix.utils.config import get_device
from PIL import Image
import base64
from natix.protocol import ExtendedImageSynapse
import io
from natix.base.miner import BaseMinerNeuron
import bittensor as bt
import typing
class Miner(BaseMinerNeuron):
def __init__(sel... |
class Miner(BaseMinerNeuron):
def __init__(self, config=None):
pass
def load_image_detector(self):
pass
async def forward_image(self, synapse: ExtendedImageSynapse) -> ExtendedImageSynapse:
'''
Perform inference on image
Args:
synapse (bt.Synapse): The... | 7 | 1 | 11 | 1 | 8 | 1 | 2 | 0.16 | 1 | 6 | 1 | 0 | 6 | 1 | 6 | 48 | 73 | 14 | 51 | 12 | 44 | 8 | 41 | 11 | 34 | 4 | 6 | 2 | 11 |
326,498 | natixnetwork/streetvision-subnet | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/natixnetwork_streetvision-subnet/neurons/validator_proxy.py | neurons.validator_proxy.ValidatorProxy | from natix.validator.proxy import ProxyCounter
from natix.validator.organic_task_distributor import OrganicTaskDistributor
import socket
from concurrent.futures import ThreadPoolExecutor
from natix.protocol import prepare_synapse
from cryptography.hazmat.primitives.asymmetric.ed25519 import Ed25519PublicKey
import base... |
class ValidatorProxy:
def __init__(self, validator):
pass
def get_credentials(self):
pass
def verify_credentials(public_key_bytes):
pass
def start_server(self):
pass
def authenticate_token(self, public_key_bytes):
pass
async ... | 9 | 0 | 25 | 3 | 21 | 0 | 3 | 0.02 | 0 | 12 | 2 | 0 | 7 | 9 | 7 | 7 | 197 | 30 | 164 | 44 | 152 | 3 | 119 | 37 | 110 | 9 | 0 | 3 | 25 |
326,499 | circuit-synth/circuit-synth | /Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/circuit-synth_circuit-synth/src/circuit_synth/ai_integration/claude/agent_registry.py | circuit_synth.ai_integration.claude.agent_registry.CircuitSubAgent | import json
from typing import Any, Callable, Dict, List, Optional
class CircuitSubAgent:
"""Represents a circuit design sub-agent"""
def __init__(self, name: str, description: str, system_prompt: str, allowed_tools: List[str], expertise_area: str, model: Optional[str]=None):
self.name = name
... |
class CircuitSubAgent:
'''Represents a circuit design sub-agent'''
def __init__(self, name: str, description: str, system_prompt: str, allowed_tools: List[str], expertise_area: str, model: Optional[str]=None):
pass
def to_markdown(self) -> str:
'''Convert agent to Claude Code markdown for... | 3 | 2 | 18 | 2 | 16 | 1 | 3 | 0.09 | 0 | 2 | 0 | 0 | 2 | 6 | 2 | 2 | 40 | 5 | 32 | 20 | 21 | 3 | 19 | 12 | 16 | 4 | 0 | 2 | 5 |
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