hot-spot-analysis 1.0.4b0

Write a python function to calculate your metric(s), and run it over all clusters in your data. Find the cuts of the data driving your metrics.

hot-spot-analysis

A brief description of your package.

Installation

pip install hot-spot-analysis

Python Import

from hot_spot_analysis.hot_spot_analysis import HotSpotAnalyzer
HSA = HotSpotAnalyzer(...)

Quickstart

Short Theoretical Demonstration:

If we have 3 columns [a, b, c], and we want to cut our data using those columns we would have to group our data as such to know all of the interactions' impact on our metric of interest. And this problem becomes increasingly complicated as we increase the number of columns.

Interacting 3 columns: [a, b, c] -> 7 valid data cuts

• @ depth = 1: [a,b,c] <- 3 data cuts
• @ depth = 2: [ab,ac,bc] <- 3 data cuts
• @ depth = 3: [abc] <- 1 data cuts

A simple example of Hot Spot Analysis (HSA)

Example - Input Data

column1	column2	Value
A	X	10
A	Y	20
B	X	30
B	Y	40
C	X	50
C	Y	60

Example - Simple metric function

# Metric function
def metric_function(group):
    return {
        'sum_value': group['Value'].sum()
    }

Example Run HSA

from hot_spot_analysis.hot_spot_analysis import HotSpotAnalyzer

HSA = HotSpotAnalyzer(
    data=example_data,                  # See above
    target_cols=["column1", "column2"], 
    objective_function=metric_function, # See above
)

HSA.run_hsa()
hsa_data = HSA.export_hsa_output_df()
print(hsa_data.head(10))

Below is a simplified example of the HSA output

group	n_rows	sum_value
{'column1': 'A'}	2	30
{'column1': 'B'}	2	70
{'column1': 'C'}	2	110
{'column2': 'X'}	3	90
{'column2': 'Y'}	3	120
{'column1': 'A', 'column2': 'X'}	1	10
{'column1': 'A', 'column2': 'Y'}	1	20
{'column1': 'B', 'column2': 'X'}	1	30
{'column1': 'B', 'column2': 'Y'}	1	40
{'column1': 'C', 'column2': 'X'}	1	50
{'column1': 'C', 'column2': 'Y'}	1	60