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merged 2 commits into from
Sep 27, 2023
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Update k_means_clust.py #8996

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17d6427
Update k_means_clust.py
thor-harsh Aug 19, 2023
f06bee6
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tianyizheng02 Sep 27, 2023
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23 changes: 10 additions & 13 deletions machine_learning/k_means_clust.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,10 @@
- initial_centroids , initial centroid values generated by utility function(mentioned
in usage).
- 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.
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,
initial_centroids = get_initial_centroids(
X,
Expand All @@ -31,8 +31,8 @@
record_heterogeneity=heterogeneity,
verbose=True # whether to print logs in console or not.(default=False)
)
4. Plot the loss function, hetrogeneity values for every iteration saved in
hetrogeneity list.
4. Plot the loss function and heterogeneity values for every iteration saved in
heterogeneity list.
plot_heterogeneity(
heterogeneity,
k
Expand Down Expand Up @@ -198,13 +198,10 @@ def report_generator(
df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
) -> pd.DataFrame:
"""
Function generates easy-erading clustering report. It takes 2 arguments as an input:
DataFrame - dataframe with predicted cluester column;
FillMissingReport - dictionary of rules how we are going to fill missing
values of for final report generate (not included in modeling);
in order to run the function following libraries must be imported:
import pandas as pd
import numpy as np
Generates a clustering report. This function takes 2 arguments as input:
df - dataframe with predicted cluster column
fill_missing_report - dictionary of rules on how we are going to fill in missing
values for final generated report (not included in modelling);
>>> data = pd.DataFrame()
>>> data['numbers'] = [1, 2, 3]
>>> data['col1'] = [0.5, 2.5, 4.5]
Expand Down Expand Up @@ -306,10 +303,10 @@ def report_generator(
a.columns = report.columns # rename columns to match report
report = report.drop(
report[report.Type == "count"].index
) # drop count values except cluster size
) # drop count values except for cluster size
report = pd.concat(
[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)
cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1]
Expand Down