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All themes refactored to use vector masks instead of iterrows
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mwisnowski
parent
17379340a8
commit
6de2239a47
2 changed files with 205 additions and 64 deletions
227
tagger.py
227
tagger.py
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@ -5641,72 +5641,173 @@ def tag_for_toughness(df: pd.DataFrame, color: str) -> None:
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except Exception as e:
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logging.error(f'Error in tag_for_toughness: {str(e)}')
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raise
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## Topdeck
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def tag_for_topdeck(df, color):
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print(f'Tagging cards in {color}_cards.csv that fit the "Topdeck" theme.')
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for index, row in df.iterrows():
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theme_tags = row['themeTags']
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if pd.notna(row['text']):
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if ('from the top' in row['text'].lower()
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or 'look at the top' in row['text'].lower()
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or 'reveal the top' in row['text'].lower()
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or 'scries' in row['text'].lower()
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or 'surveils' in row['text'].lower()
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or 'top of your library' in row['text'].lower()
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or 'you scry' in row['text'].lower()
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or 'you surveil' in row['text'].lower()
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):
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tag_type = ['Topdeck']
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for tag in tag_type:
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if tag not in theme_tags:
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theme_tags.extend([tag])
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df.at[index, 'themeTags'] = theme_tags
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if pd.notna(row['keywords']):
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if ('Miracle' in row['keywords']
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or 'Scry' in row['keywords']
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or 'Surveil' in row['keywords']
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):
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tag_type = ['Topdeck']
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for tag in tag_type:
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if tag not in theme_tags:
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theme_tags.extend([tag])
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df.at[index, 'themeTags'] = theme_tags
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print(f'"Topdeck" cards in {color}_cards.csv have been tagged.\n')
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def create_topdeck_text_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for cards with topdeck-related text patterns.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards have topdeck text patterns
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"""
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return utility.create_text_mask(df, settings.TOPDECK_TEXT_PATTERNS)
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def create_topdeck_keyword_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for cards with topdeck-related keywords.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards have topdeck keywords
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"""
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return utility.create_keyword_mask(df, settings.TOPDECK_KEYWORDS)
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def create_topdeck_specific_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for specific topdeck-related cards.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards are specific topdeck cards
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"""
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return utility.create_name_mask(df, settings.TOPDECK_SPECIFIC_CARDS)
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def create_topdeck_exclusion_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for cards that should be excluded from topdeck effects.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards should be excluded
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"""
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return utility.create_text_mask(df, settings.TOPDECK_EXCLUSION_PATTERNS)
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def tag_for_topdeck(df: pd.DataFrame, color: str) -> None:
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"""Tag cards that manipulate the top of library using vectorized operations.
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This function identifies and tags cards that interact with the top of the library including:
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- Cards that look at or reveal top cards
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- Cards with scry or surveil effects
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- Cards with miracle or similar mechanics
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- Cards that care about the order of the library
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Args:
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df: DataFrame containing card data
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color: Color identifier for logging purposes
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Raises:
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ValueError: If required DataFrame columns are missing
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"""
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start_time = pd.Timestamp.now()
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logging.info(f'Starting topdeck effect tagging for {color}_cards.csv')
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try:
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# Validate required columns
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required_cols = {'text', 'themeTags', 'keywords'}
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utility.validate_dataframe_columns(df, required_cols)
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# Create masks for different topdeck patterns
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text_mask = create_topdeck_text_mask(df)
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keyword_mask = create_topdeck_keyword_mask(df)
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specific_mask = create_topdeck_specific_mask(df)
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exclusion_mask = create_topdeck_exclusion_mask(df)
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# Combine masks
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final_mask = (text_mask | keyword_mask | specific_mask) & ~exclusion_mask
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# Apply tags
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utility.apply_tag_vectorized(df, final_mask, ['Topdeck'])
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# Log results
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duration = (pd.Timestamp.now() - start_time).total_seconds()
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logging.info(f'Tagged {final_mask.sum()} cards with topdeck effects in {duration:.2f}s')
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except Exception as e:
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logging.error(f'Error in tag_for_topdeck: {str(e)}')
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raise
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## X Spells
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def tag_for_x_spells(df, color):
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print(f'Tagging cards in {color}_cards.csv that fit the "X Spells" theme.')
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df['manaCost'] = df['manaCost'].astype(str)
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for index, row in df.iterrows():
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theme_tags = row['themeTags']
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if pd.notna(row['text']):
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if ('cost {x} less' in row['text'].lower()
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or 'don\'t lose this' in row['text'].lower()
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or 'don\'t lose unspent' in row['text'].lower()
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or 'lose unused mana' in row['text'].lower()
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or 'unused mana would empty' in row['text'].lower()
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or 'with {x} in its' in row['text'].lower()
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or 'you cast cost {1} less' in row['text'].lower()
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or 'you cast cost {2} less' in row['text'].lower()
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or 'you cast cost {3} less' in row['text'].lower()
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or 'you cast cost {4} less' in row['text'].lower()
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or 'you cast cost {5} less' in row['text'].lower()
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):
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tag_type = ['X Spells']
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for tag in tag_type:
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if tag not in theme_tags:
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theme_tags.extend([tag])
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df.at[index, 'themeTags'] = theme_tags
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if ('{X}' in row['manaCost']
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):
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tag_type = ['X Spells']
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for tag in tag_type:
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if tag not in theme_tags:
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theme_tags.extend([tag])
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df.at[index, 'themeTags'] = theme_tags
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print(f'"X Spells" cards in {color}_cards.csv have been tagged.\n')
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def create_x_spells_text_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for cards with X spell-related text patterns.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards have X spell text patterns
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"""
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text_patterns = [
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'cost {x} less',
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'don\'t lose this',
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'don\'t lose unspent',
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'lose unused mana',
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'unused mana would empty',
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'with {x} in its',
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'you cast cost {1} less',
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'you cast cost {2} less',
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'you cast cost {3} less',
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'you cast cost {4} less',
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'you cast cost {5} less'
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]
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return utility.create_text_mask(df, text_patterns)
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def create_x_spells_mana_mask(df: pd.DataFrame) -> pd.Series:
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"""Create a boolean mask for cards with X in their mana cost.
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Args:
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df: DataFrame to search
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Returns:
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Boolean Series indicating which cards have X in mana cost
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"""
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return df['manaCost'].fillna('').str.contains('{X}', case=True, regex=False)
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def tag_for_x_spells(df: pd.DataFrame, color: str) -> None:
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"""Tag cards that care about X spells using vectorized operations.
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This function identifies and tags cards that:
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- Have X in their mana cost
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- Care about X spells or mana values
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- Have cost reduction effects for X spells
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- Preserve unspent mana
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Args:
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df: DataFrame containing card data
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color: Color identifier for logging purposes
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Raises:
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ValueError: If required DataFrame columns are missing
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"""
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start_time = pd.Timestamp.now()
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logging.info(f'Starting X spells tagging for {color}_cards.csv')
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try:
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# Validate required columns
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required_cols = {'text', 'themeTags', 'manaCost'}
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utility.validate_dataframe_columns(df, required_cols)
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# Create masks for different X spell patterns
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text_mask = create_x_spells_text_mask(df)
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mana_mask = create_x_spells_mana_mask(df)
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# Combine masks
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final_mask = text_mask | mana_mask
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# Apply tags
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utility.apply_tag_vectorized(df, final_mask, ['X Spells'])
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# Log results
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duration = (pd.Timestamp.now() - start_time).total_seconds()
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logging.info(f'Tagged {final_mask.sum()} cards with X spell effects in {duration:.2f}s')
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except Exception as e:
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logging.error(f'Error in tag_for_x_spells: {str(e)}')
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raise
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### Interaction
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## Overall tag for interaction group
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