diff --git a/README.md b/README.md
index 5947f39b2..af6647ceb 100644
--- a/README.md
+++ b/README.md
@@ -159,9 +159,9 @@ import pandas as pd
from IPython.display import display
# Load real-world data:
-df_reference, df_analysis, _ = nml.load_us_census_ma_employment_data()
-display(df_reference.head())
-display(df_analysis.head())
+reference_df, analysis_df, _ = nml.load_us_census_ma_employment_data()
+display(reference_df.head())
+display(analysis_df.head())
# Choose a chunker or set a chunk size:
chunk_size = 5000
@@ -175,8 +175,8 @@ estimator = nml.CBPE(
metrics=['roc_auc'],
chunk_size=chunk_size,
)
-estimator = estimator.fit(df_reference)
-estimated_performance = estimator.estimate(df_analysis)
+estimator = estimator.fit(reference_df)
+estimated_performance = estimator.estimate(analysis_df)
# Show results:
figure = estimated_performance.plot()
@@ -192,8 +192,8 @@ univariate_calculator = nml.UnivariateDriftCalculator(
chunk_size=chunk_size
)
-univariate_calculator.fit(df_reference)
-univariate_drift = univariate_calculator.calculate(df_analysis)
+univariate_calculator.fit(reference_df)
+univariate_drift = univariate_calculator.calculate(analysis_df)
# Get features that drift the most with count-based ranker:
alert_count_ranker = nml.AlertCountRanker()
@@ -214,10 +214,10 @@ figure = univariate_drift.filter(period='analysis', column_names=['RELP','AGEP',
figure.show()
# Get target data, calculate, plot and compare realized performance with estimated performance:
-_, _, analysis_targets = nml.load_us_census_ma_employment_data()
+_, _, analysis_targets_df = nml.load_us_census_ma_employment_data()
-df_analysis_with_targets = pd.concat([df_analysis, analysis_targets], axis=1)
-display(df_analysis_with_targets.head())
+analysis_with_targets_df = pd.concat([analysis_df, analysis_targets_df], axis=1)
+display(analysis_with_targets_df.head())
performance_calculator = nml.PerformanceCalculator(
problem_type='classification_binary',
@@ -227,8 +227,8 @@ performance_calculator = nml.PerformanceCalculator(
metrics=['roc_auc'],
chunk_size=chunk_size)
-performance_calculator.fit(df_reference)
-calculated_performance = performance_calculator.calculate(df_analysis_with_targets)
+performance_calculator.fit(reference_df)
+calculated_performance = performance_calculator.calculate(analysis_with_targets_df)
figure = estimated_performance.filter(period='analysis').compare(calculated_performance).plot()
figure.show()
diff --git a/docs/_static/butterfly-multivariate-drift.svg b/docs/_static/butterfly-multivariate-drift.svg
index 95da1c94d..a178f9ea5 100644
--- a/docs/_static/butterfly-multivariate-drift.svg
+++ b/docs/_static/butterfly-multivariate-drift.svg
@@ -1 +1 @@
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/docs/_static/butterfly-scatterplot.svg b/docs/_static/butterfly-scatterplot.svg
index 38073e51e..d8a465809 100644
--- a/docs/_static/butterfly-scatterplot.svg
+++ b/docs/_static/butterfly-scatterplot.svg
@@ -6,7 +6,7 @@
- 2023-11-13T16:38:14.707755
+ 2023-11-16T09:35:53.995514
image/svg+xml
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