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add visualization of k means clustering as excel format (#2104)
* add visualization of kmneas clust as excel format * style changes * style changes * Add doctest and typehint! * style change * Update machine_learning/k_means_clust.py Co-authored-by: Christian Clauss <cclauss@me.com> * Update machine_learning/k_means_clust.py Co-authored-by: Christian Clauss <cclauss@me.com> Co-authored-by: Christian Clauss <cclauss@me.com>
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@ -47,12 +47,18 @@ Usage:
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k
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)
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5. Have fun..
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5. Transfers Dataframe into excel format it must have feature called
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'Clust' with k means clustering numbers in it.
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"""
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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from sklearn.metrics import pairwise_distances
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import warnings
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warnings.filterwarnings("ignore")
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TAG = "K-MEANS-CLUST/ "
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@ -202,3 +208,158 @@ if False: # change to true to run this test case.
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verbose=True,
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)
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plot_heterogeneity(heterogeneity, k)
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def ReportGenerator(
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df: pd.DataFrame, ClusteringVariables: np.array, FillMissingReport=None
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) -> pd.DataFrame:
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"""
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Function generates easy-erading clustering report. It takes 2 arguments as an input:
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DataFrame - dataframe with predicted cluester column;
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FillMissingReport - dictionary of rules how we are going to fill missing
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values of for final report generate (not included in modeling);
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in order to run the function following libraries must be imported:
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import pandas as pd
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import numpy as np
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>>> data = pd.DataFrame()
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>>> data['numbers'] = [1, 2, 3]
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>>> data['col1'] = [0.5, 2.5, 4.5]
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>>> data['col2'] = [100, 200, 300]
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>>> data['col3'] = [10, 20, 30]
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>>> data['Cluster'] = [1, 1, 2]
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>>> ReportGenerator(data, ['col1', 'col2'], 0)
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Features Type Mark 1 2
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0 # of Customers ClusterSize False 2.000000 1.000000
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1 % of Customers ClusterProportion False 0.666667 0.333333
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2 col1 mean_with_zeros True 1.500000 4.500000
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3 col2 mean_with_zeros True 150.000000 300.000000
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4 numbers mean_with_zeros False 1.500000 3.000000
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.. ... ... ... ... ...
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99 dummy 5% False 1.000000 1.000000
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100 dummy 95% False 1.000000 1.000000
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101 dummy stdev False 0.000000 NaN
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102 dummy mode False 1.000000 1.000000
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103 dummy median False 1.000000 1.000000
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<BLANKLINE>
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[104 rows x 5 columns]
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"""
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# Fill missing values with given rules
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if FillMissingReport:
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df.fillna(value=FillMissingReport, inplace=True)
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df["dummy"] = 1
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numeric_cols = df.select_dtypes(np.number).columns
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report = (
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df.groupby(["Cluster"])[ # constract report dataframe
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numeric_cols
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] # group by cluster number
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.agg(
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[
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("sum", np.sum),
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("mean_with_zeros", lambda x: np.mean(np.nan_to_num(x))),
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("mean_without_zeros", lambda x: x.replace(0, np.NaN).mean()),
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(
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"mean_25-75",
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lambda x: np.mean(
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np.nan_to_num(
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sorted(x)[
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round((len(x) * 25 / 100)) : round(len(x) * 75 / 100)
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]
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)
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),
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),
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("mean_with_na", np.mean),
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("min", lambda x: x.min()),
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("5%", lambda x: x.quantile(0.05)),
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("25%", lambda x: x.quantile(0.25)),
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("50%", lambda x: x.quantile(0.50)),
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("75%", lambda x: x.quantile(0.75)),
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("95%", lambda x: x.quantile(0.95)),
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("max", lambda x: x.max()),
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("count", lambda x: x.count()),
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("stdev", lambda x: x.std()),
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("mode", lambda x: x.mode()[0]),
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("median", lambda x: x.median()),
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("# > 0", lambda x: (x > 0).sum()),
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]
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)
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.T.reset_index()
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.rename(index=str, columns={"level_0": "Features", "level_1": "Type"})
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) # rename columns
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clustersize = report[
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(report["Features"] == "dummy") & (report["Type"] == "count")
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] # caclulating size of cluster(count of clientID's)
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clustersize.Type = (
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"ClusterSize" # rename created cluster df to match report column names
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)
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clustersize.Features = "# of Customers"
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clusterproportion = pd.DataFrame(
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clustersize.iloc[:, 2:].values
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/ clustersize.iloc[:, 2:].values.sum() # caclulating proportion of cluster
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)
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clusterproportion[
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"Type"
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] = "% of Customers" # rename created cluster df to match report column names
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clusterproportion["Features"] = "ClusterProportion"
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cols = clusterproportion.columns.tolist()
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cols = cols[-2:] + cols[:-2]
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clusterproportion = clusterproportion[cols] # rearrange columns to match report
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clusterproportion.columns = report.columns
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a = pd.DataFrame(
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abs(
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report[report["Type"] == "count"].iloc[:, 2:].values
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- clustersize.iloc[:, 2:].values
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)
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) # generating df with count of nan values
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a["Features"] = 0
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a["Type"] = "# of nan"
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a.Features = report[
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report["Type"] == "count"
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].Features.tolist() # filling values in order to match report
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cols = a.columns.tolist()
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cols = cols[-2:] + cols[:-2]
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a = a[cols] # rearrange columns to match report
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a.columns = report.columns # rename columns to match report
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report = report.drop(
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report[report.Type == "count"].index
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) # drop count values except cluster size
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report = pd.concat(
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[report, a, clustersize, clusterproportion], axis=0
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) # concat report with clustert size and nan values
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report["Mark"] = report["Features"].isin(ClusteringVariables)
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cols = report.columns.tolist()
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cols = cols[0:2] + cols[-1:] + cols[2:-1]
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report = report[cols]
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sorter1 = {
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"ClusterSize": 9,
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"ClusterProportion": 8,
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"mean_with_zeros": 7,
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"mean_with_na": 6,
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"max": 5,
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"50%": 4,
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"min": 3,
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"25%": 2,
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"75%": 1,
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"# of nan": 0,
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"# > 0": -1,
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"sum_with_na": -2,
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}
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report = (
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report.assign(
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Sorter1=lambda x: x.Type.map(sorter1),
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Sorter2=lambda x: list(reversed(range(len(x)))),
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)
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.sort_values(["Sorter1", "Mark", "Sorter2"], ascending=False)
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.drop(["Sorter1", "Sorter2"], axis=1)
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)
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report.columns.name = ""
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report = report.reset_index()
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report.drop(columns=["index"], inplace=True)
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return report
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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