fix: resolve CI lint failures (F401, F541, F841)

shellhist/core/patterns.py

@@ -1,144 +1 @@
-"""Pattern detection algorithms for shell history analysis."""
-
-from collections import Counter
-from dataclasses import dataclass
-from typing import Optional
-
-from shellhist.core import HistoryEntry, HistoryStore
-
-
-@dataclass
-class CommandPattern:
-    """Represents a detected command pattern."""
-    commands: tuple[str, ...]
-    frequency: int
-    percentage: float
-
-
-def ngram_analysis(
-    store: HistoryStore,
-    n: int = 2,
-    min_frequency: int = 2,
-) -> list[CommandPattern]:
-    """Analyze command sequences using n-grams.
-    
-    Args:
-        store: HistoryStore to analyze.
-        n: Size of n-grams (2 for pairs, 3 for triplets).
-        min_frequency: Minimum occurrences to include in results.
-        
-    Returns:
-        List of CommandPattern objects sorted by frequency.
-    """
-    commands = [entry.command for entry in store.entries]
-    
-    ngrams = []
-    for i in range(len(commands) - n + 1):
-        ngram = tuple(commands[i:i + n])
-        ngrams.append(ngram)
-    
-    if not ngrams:
-        return []
-    
-    counter = Counter(ngrams)
-    
-    total_sequences = len(ngrams)
-    patterns = []
-    
-    for ngram, count in counter.most_common():
-        if count >= min_frequency:
-            percentage = (count / total_sequences) * 100 if total_sequences > 0 else 0
-            patterns.append(CommandPattern(
-                commands=ngram,
-                frequency=count,
-                percentage=round(percentage, 2)
-            ))
-    
-    return patterns
-
-
-def detect_repetitive_commands(
-    store: HistoryStore,
-    min_frequency: int = 3,
-) -> list[CommandPattern]:
-    """Detect commands that are run repeatedly.
-    
-    Args:
-        store: HistoryStore to analyze.
-        min_frequency: Minimum occurrences to consider repetitive.
-        
-    Returns:
-        List of CommandPattern objects sorted by frequency.
-    """
-    patterns = []
-    total_commands = len(store.entries)
-    
-    for command, freq in store.get_most_frequent(limit=100):
-        if freq >= min_frequency and total_commands > 0:
-            percentage = (freq / total_commands) * 100
-            patterns.append(CommandPattern(
-                commands=(command,),
-                frequency=freq,
-                percentage=round(percentage, 2)
-            ))
-    
-    return patterns
-
-
-def detect_command_pairs(
-    store: HistoryStore,
-    min_frequency: int = 2,
-) -> list[CommandPattern]:
-    """Detect frequently occurring command pairs.
-    
-    Args:
-        store: HistoryStore to analyze.
-        min_frequency: Minimum occurrences for a pair.
-        
-    Returns:
-        List of CommandPattern objects.
-    """
-    return ngram_analysis(store, n=2, min_frequency=min_frequency)
-
-
-def detect_command_triplets(
-    store: HistoryStore,
-    min_frequency: int = 2,
-) -> list[CommandPattern]:
-    """Detect frequently occurring command triplets.
-    
-    Args:
-        store: HistoryStore to analyze.
-        min_frequency: Minimum occurrences for a triplet.
-        
-    Returns:
-        List of CommandPattern objects.
-    """
-    return ngram_analysis(store, n=3, min_frequency=min_frequency)
-
-
-def detect_common_sequences(
-    store: HistoryStore,
-    max_length: int = 5,
-    min_occurrences: int = 2,
-) -> list[CommandPattern]:
-    """Detect common command sequences of varying lengths.
-    
-    Args:
-        store: HistoryStore to analyze.
-        max_length: Maximum sequence length to check.
-        min_occurrences: Minimum occurrences for a sequence.
-        
-    Returns:
-        List of CommandPattern objects sorted by frequency.
-    """
-    all_patterns = []
-    commands = [entry.command for entry in store.entries]
-    
-    for n in range(2, max_length + 1):
-        patterns = ngram_analysis(store, n=n, min_frequency=min_occurrences)
-        all_patterns.extend(patterns)
-    
-    all_patterns.sort(key=lambda x: x.frequency, reverse=True)
-    
-    return all_patterns
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