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Update k_means_clust.py (#8996)

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* Update k_means_clust.py

* Apply suggestions from code review

---------

Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com>
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thor-harsh 2023-09-27 12:01:42 +05:30 committed by GitHub
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1 changed files with 10 additions and 13 deletions
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machine_learning/k_means_clust.py
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@ -11,10 +11,10 @@ Inputs:
- initial_centroids , initial centroid values generated by utility function(mentioned - initial_centroids , initial centroid values generated by utility function(mentioned
in usage). in usage).
- maxiter , maximum number of iterations to process. - maxiter , maximum number of iterations to process.
- heterogeneity , empty list that will be filled with hetrogeneity values if passed - heterogeneity , empty list that will be filled with heterogeneity values if passed
to kmeans func. to kmeans func.
Usage: Usage:
1. define 'k' value, 'X' features array and 'hetrogeneity' empty list 1. define 'k' value, 'X' features array and 'heterogeneity' empty list
2. create initial_centroids, 2. create initial_centroids,
initial_centroids = get_initial_centroids( initial_centroids = get_initial_centroids(
X, X,
@ -31,8 +31,8 @@ Usage:
record_heterogeneity=heterogeneity, record_heterogeneity=heterogeneity,
verbose=True # whether to print logs in console or not.(default=False) verbose=True # whether to print logs in console or not.(default=False)
) )
4. Plot the loss function, hetrogeneity values for every iteration saved in 4. Plot the loss function and heterogeneity values for every iteration saved in
hetrogeneity list. heterogeneity list.
plot_heterogeneity( plot_heterogeneity(
heterogeneity, heterogeneity,
k k
@ -198,13 +198,10 @@ def report_generator(
df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
) -> pd.DataFrame: ) -> pd.DataFrame:
""" """
Function generates easy-erading clustering report. It takes 2 arguments as an input: Generates a clustering report. This function takes 2 arguments as input:
DataFrame - dataframe with predicted cluester column; df - dataframe with predicted cluster column
FillMissingReport - dictionary of rules how we are going to fill missing fill_missing_report - dictionary of rules on how we are going to fill in missing
values of for final report generate (not included in modeling); values for final generated report (not included in modelling);
in order to run the function following libraries must be imported:
import pandas as pd
import numpy as np
>>> data = pd.DataFrame() >>> data = pd.DataFrame()
>>> data['numbers'] = [1, 2, 3] >>> data['numbers'] = [1, 2, 3]
>>> data['col1'] = [0.5, 2.5, 4.5] >>> data['col1'] = [0.5, 2.5, 4.5]
@ -306,10 +303,10 @@ def report_generator(
a.columns = report.columns # rename columns to match report a.columns = report.columns # rename columns to match report
report = report.drop( report = report.drop(
report[report.Type == "count"].index report[report.Type == "count"].index
) # drop count values except cluster size ) # drop count values except for cluster size
report = pd.concat( report = pd.concat(
[report, a, clustersize, clusterproportion], axis=0 [report, a, clustersize, clusterproportion], axis=0
) # concat report with clustert size and nan values ) # concat report with cluster size and nan values
report["Mark"] = report["Features"].isin(clustering_variables) report["Mark"] = report["Features"].isin(clustering_variables)
cols = report.columns.tolist() cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1] cols = cols[0:2] + cols[-1:] + cols[2:-1]