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Ruff pandas vet (#10281)

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* Python linting: Add ruff rules for Pandas-vet and Pytest-style

* updating DIRECTORY.md

---------

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Christian Clauss
2023-10-11 20:30:02 +02:00
committed by GitHub
parent d5323dbaee
commit 3f094fe49d
28 changed files with 260 additions and 241 deletions
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4
machine_learning/dimensionality_reduction.py
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@ -169,7 +169,7 @@ def test_linear_discriminant_analysis() -> None:
dimensions = 2
# Assert that the function raises an AssertionError if dimensions > classes
with pytest.raises(AssertionError) as error_info:
with pytest.raises(AssertionError) as error_info: # noqa: PT012
projected_data = linear_discriminant_analysis(
features, labels, classes, dimensions
)
@ -185,7 +185,7 @@ def test_principal_component_analysis() -> None:
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
with pytest.raises(AssertionError) as error_info:
with pytest.raises(AssertionError) as error_info: # noqa: PT012
output = principal_component_analysis(features, dimensions)
if not np.allclose(expected_output, output):
raise AssertionError

69
machine_learning/k_means_clust.py
View File

@ -128,7 +128,7 @@ def plot_heterogeneity(heterogeneity, k):
def kmeans(
data, k, initial_centroids, maxiter=500, record_heterogeneity=None, verbose=False
):
"""This function runs k-means on given data and initial set of centroids.
"""Runs k-means on given data and initial set of centroids.
maxiter: maximum number of iterations to run.(default=500)
record_heterogeneity: (optional) a list, to store the history of heterogeneity
as function of iterations
@ -195,20 +195,20 @@ if False: # change to true to run this test case.
def report_generator(
df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
predicted: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
) -> pd.DataFrame:
"""
Generates a clustering report. This function takes 2 arguments as input:
df - dataframe with predicted cluster column
Generate a clustering report given these two arguments:
predicted - 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]
>>> data['col2'] = [100, 200, 300]
>>> data['col3'] = [10, 20, 30]
>>> data['Cluster'] = [1, 1, 2]
>>> report_generator(data, ['col1', 'col2'], 0)
>>> predicted = pd.DataFrame()
>>> predicted['numbers'] = [1, 2, 3]
>>> predicted['col1'] = [0.5, 2.5, 4.5]
>>> predicted['col2'] = [100, 200, 300]
>>> predicted['col3'] = [10, 20, 30]
>>> predicted['Cluster'] = [1, 1, 2]
>>> report_generator(predicted, ['col1', 'col2'], 0)
Features Type Mark 1 2
0 # of Customers ClusterSize False 2.000000 1.000000
1 % of Customers ClusterProportion False 0.666667 0.333333
@ -226,11 +226,11 @@ def report_generator(
"""
# Fill missing values with given rules
if fill_missing_report:
df = df.fillna(value=fill_missing_report)
df["dummy"] = 1
numeric_cols = df.select_dtypes(np.number).columns
predicted = predicted.fillna(value=fill_missing_report)
predicted["dummy"] = 1
numeric_cols = predicted.select_dtypes(np.number).columns
report = (
df.groupby(["Cluster"])[ # construct report dataframe
predicted.groupby(["Cluster"])[ # construct report dataframe
numeric_cols
] # group by cluster number
.agg(
@ -267,46 +267,43 @@ def report_generator(
.rename(index=str, columns={"level_0": "Features", "level_1": "Type"})
) # rename columns
# calculate the size of cluster(count of clientID's)
# avoid SettingWithCopyWarning
clustersize = report[
(report["Features"] == "dummy") & (report["Type"] == "count")
].copy() # avoid SettingWithCopyWarning
clustersize.Type = (
"ClusterSize" # rename created cluster df to match report column names
)
].copy()
# rename created predicted cluster to match report column names
clustersize.Type = "ClusterSize"
clustersize.Features = "# of Customers"
# calculating the proportion of cluster
clusterproportion = pd.DataFrame(
clustersize.iloc[:, 2:].values
/ clustersize.iloc[:, 2:].values.sum() # calculating the proportion of cluster
clustersize.iloc[:, 2:].to_numpy() / clustersize.iloc[:, 2:].to_numpy().sum()
)
clusterproportion[
"Type"
] = "% of Customers" # rename created cluster df to match report column names
# rename created predicted cluster to match report column names
clusterproportion["Type"] = "% of Customers"
clusterproportion["Features"] = "ClusterProportion"
cols = clusterproportion.columns.tolist()
cols = cols[-2:] + cols[:-2]
clusterproportion = clusterproportion[cols] # rearrange columns to match report
clusterproportion.columns = report.columns
# generating dataframe with count of nan values
a = pd.DataFrame(
abs(
report[report["Type"] == "count"].iloc[:, 2:].values
- clustersize.iloc[:, 2:].values
report[report["Type"] == "count"].iloc[:, 2:].to_numpy()
- clustersize.iloc[:, 2:].to_numpy()
)
) # generating df with count of nan values
)
a["Features"] = 0
a["Type"] = "# of nan"
a.Features = report[
report["Type"] == "count"
].Features.tolist() # filling values in order to match report
# filling values in order to match report
a.Features = report[report["Type"] == "count"].Features.tolist()
cols = a.columns.tolist()
cols = cols[-2:] + cols[:-2]
a = a[cols] # rearrange columns to match report
a.columns = report.columns # rename columns to match report
report = report.drop(
report[report.Type == "count"].index
) # drop count values except for cluster size
report = pd.concat(
[report, a, clustersize, clusterproportion], axis=0
) # concat report with cluster size and nan values
# drop count values except for cluster size
report = report.drop(report[report.Type == "count"].index)
# concat report with cluster size and nan values
report = pd.concat([report, a, clustersize, clusterproportion], axis=0)
report["Mark"] = report["Features"].isin(clustering_variables)
cols = report.columns.tolist()
cols = cols[0:2] + cols[-1:] + cols[2:-1]