nwc-pattern-miner 0.2

Mines sequence patterns co-occuring with non-compliant windows in a target feature.

nwc-pattern-mining

Non-compliant Window Co-occurrence pattern mining in temporal data

• A Python library to find sequential association patterns in time-series data, co-occurring with target anomalous windows.
• Anomalous windows are sequences in which a target variable defies expected behavior (e.g. Emissions from a vehicle).
• The patterns are found in co-occurrence with a specific non-compliant feature (to decipher the reason behinds it's irregular behavior).
• The package uses various pruning methodologies to speed up pattern mining, and hashing for quick support count.
• The algorithm is based on research: (Discovering non-compliant window co-occurrence patterns)[https://link.springer.com/article/10.1007/s10707-016-0289-3]

API-description:

from nwc_pattern_miner import mine_sequence_patterns

API Parameters:

• series_df: pd.DataFrame; Input DataFrame (Only features [discretized] and Target [binarized] columns)
• nc_window_col: str; Column Name with Binary Target (Anomalous Windows)
• support_threshold: float; Support threshold for sequence co-occurrence patterns
• crossk_threshold: float; Ripley's Cross-k threshold for sequence co-occurrence patterns
• pattern_length: int; length of feature sequences co-occurring with anomalous windows
• confidence_threshold: float, default=-1; Confidence threshold for sequence co-occurrence patterns
• lag: int, default= 0; lag consideration between sequence patterns and anomalous windows
• invalid_seq_indexes: list, default=list(); list of indexes across which sequence patterns would be invalidated
• output_metric: {'crossk', 'support'}, default='crossk'; Metric used to sort patterns mined
• output_type: {'topk', 'threshold'}, default='topk'; Type of output for sequence patterns mined
• output_threshold: float, default= -1; Threshold cutoff used to get output sequence patterns, if output_type='threshold'
• topk: int, default=100; Top-k sequence patterns obtained based on output_metric, if output_type='topk'
• pruning_type: str, default=apriori; Between [apriori, br-dr], both have same run-time, 'br-dr' does more enumerations but enumeration speed is much faster due to UB pruning.

Sample Input DataFrame:

engrpm	EGRkgph	MSPhum	EngTq	NCWindow
9	11	5	3	1
3	1	5	4	0

Sample Output DataFrame:

engrpm	EGRkgph	MSPhum	EngTq	Count	Support	Kvalue	Confidence	First Occurrence Index
	4 4 4	5 5 5	2 2 2	146	0.00528	2.377	1.0	47167
	4 4 4		7 7 7	250	0.00643	2.357	1.0	41984