Pandas data type 'string' not understood

[nikml]

Describe the bug
Running the Quickstart results in an error

To Reproduce
Steps to reproduce the behavior:
Runing:

import pandas as pd
import nannyml as nml
from IPython.display import display

# Load synthetic data
reference, analysis, analysis_target = nml.load_synthetic_binary_classification_dataset()
display(reference.head())
display(analysis.head())

# Choose a chunker or set a chunk size
chunk_size = 5000

# initialize, specify required data columns, fit estimator and estimate
estimator = nml.CBPE(
   y_pred_proba='y_pred_proba',
   y_pred='y_pred',
   y_true='work_home_actual',
   timestamp_column_name='timestamp',
   metrics=['roc_auc'],
   chunk_size=chunk_size,
)
estimator = estimator.fit(reference)
estimated_performance = estimator.estimate(analysis)

# Show results
figure = estimated_performance.plot(kind='performance', metric='roc_auc', plot_reference=True)
figure.show()

# Define feature columns
feature_column_names = [
    col for col in reference.columns if col not in [
        'timestamp', 'y_pred_proba', 'period', 'y_pred', 'work_home_actual', 'identifier'
    ]]

# Let's initialize the object that will perform the Univariate Drift calculations
univariate_calculator = nml.UnivariateStatisticalDriftCalculator(
    feature_column_names=feature_column_names,
    timestamp_column_name='timestamp',
    chunk_size=chunk_size
)
univariate_calculator = univariate_calculator.fit(reference)
univariate_results = univariate_calculator.calculate(analysis)
# Plot drift results for all model inputs
for feature in univariate_calculator.feature_column_names:
    figure = univariate_results.plot(
        kind='feature_drift',
        metric='statistic',
        feature_column_name=feature,
        plot_reference=True
    )
    figure.show()

# Rank features based on number of alerts
ranker = nml.Ranker.by('alert_count')
ranked_features = ranker.rank(univariate_results, only_drifting = False)
display(ranked_features)

calc = nml.StatisticalOutputDriftCalculator(
    y_pred='y_pred',
    y_pred_proba='y_pred_proba',
    timestamp_column_name='timestamp'
)
calc.fit(reference)
results = calc.calculate(analysis)

figure = results.plot(kind='prediction_drift', plot_reference=True)
figure.show()

# Let's initialize the object that will perform Data Reconstruction with PCA
rcerror_calculator = nml.DataReconstructionDriftCalculator(feature_column_names=feature_column_names, timestamp_column_name='timestamp', chunk_size=chunk_size).fit(reference_data=reference)
# let's see Reconstruction error statistics for all available data
rcerror_results = rcerror_calculator.calculate(analysis)
figure = rcerror_results.plot(kind='drift', plot_reference=True)
figure.show()

Gives the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\anaconda3\lib\site-packages\nannyml\base.py in fit(self, reference_data, *args, **kwargs)
     94             self._logger.debug(f"fitting {str(self)}")
---> 95             return self._fit(reference_data, *args, **kwargs)
     96         except InvalidArgumentsException:

~\anaconda3\lib\site-packages\nannyml\drift\model_inputs\univariate\statistical\calculator.py in _fit(self, reference_data, *args, **kwargs)
    105         self.previous_reference_data = reference_data.copy()
--> 106         self.previous_reference_results = self._calculate(self.previous_reference_data).data
    107 

~\anaconda3\lib\site-packages\nannyml\drift\model_inputs\univariate\statistical\calculator.py in _calculate(self, data, *args, **kwargs)
    116 
--> 117         self.continuous_column_names, self.categorical_column_names = _split_features_by_type(
    118             data, self.feature_column_names

~\anaconda3\lib\site-packages\nannyml\base.py in _split_features_by_type(data, feature_column_names)
    229 
--> 230     categorical_column_names = [col for col in feature_column_names if _column_is_categorical(data[col])]
    231 

~\anaconda3\lib\site-packages\nannyml\base.py in <listcomp>(.0)
    229 
--> 230     categorical_column_names = [col for col in feature_column_names if _column_is_categorical(data[col])]
    231 

~\anaconda3\lib\site-packages\nannyml\base.py in _column_is_categorical(column)
    235 def _column_is_categorical(column: pd.Series) -> bool:
--> 236     return column.dtype in ['object', 'string', 'category', 'bool']
    237 

TypeError: data type 'string' not understood

During handling of the above exception, another exception occurred:

CalculatorException                       Traceback (most recent call last)
<ipython-input-1-9ae82d7fa4d4> in <module>
     39     chunk_size=chunk_size
     40 )
---> 41 univariate_calculator = univariate_calculator.fit(reference)
     42 univariate_results = univariate_calculator.calculate(analysis)
     43 # Plot drift results for all model inputs

~\anaconda3\lib\site-packages\nannyml\base.py in fit(self, reference_data, *args, **kwargs)
     99             raise
    100         except Exception as exc:
--> 101             raise CalculatorException(f"failed while fitting {str(self)}.\n{exc}")
    102 
    103     def calculate(self, data: pd.DataFrame, *args, **kwargs) -> AbstractCalculatorResult:

CalculatorException: failed while fitting <nannyml.drift.model_inputs.univariate.statistical.calculator.UnivariateStatisticalDriftCalculator object at 0x0000022BBF196A30>.
data type 'string' not understood

Expected behavior
The quickstart code runs without a problem.

Additional context

The user who had that issue was running python 3.8 on windows through a pycharm environment.

I couldn't reproduce the error when I tried on my machine. Moreover when I guided the user to set up a new conda environment the error went away.

However maybe the way string type is defined here could be changed similar to suggestions such as these to cover more cases? I 'd hold of on that until we see more users having the issue, since in this case a misconfigured environment is more likely the problem than a library compatibility issue.

[bernhardbarker]

Looks like the required numpy version (>=1.14.0) might be wrong. It seems like it should be closer to >=1.21.0 (at least based on the current nannyml code).

I reproduced it as follows:

conda create --name nannyml python=3.8
conda activate nannyml
pip install numpy==1.19.5
pip install pandas==1.4.3
pip install nannyml==0.5.0
pip install IPython==8.4.0
# quickstart.py contains the code above
python quickstart.py  # data type 'string' not understood

These libraries all seem to be compatible with one another (otherwise pip would show a warning or automatically update libraries to compatible versions, neither of which happened).

I fixed it by updating numpy:

pip install numpy==1.21.0
python quickstart.py  # works

I can also fix the issue by just explicitly converting each column to string:

...
display(ranked_features)

for c in reference.columns:
    reference[c] = reference[c].astype("string")

for c in analysis.columns:
    analysis[c] = analysis[c].astype("string")

calc = nml.StatisticalOutputDriftCalculator(
...

I'm running Ubuntu 22, in case it matters.

[gabrieltardochi]

Commenting to keep the issue alive, since I just faced the same problem while running the Univariate Drift Detection tutorial. Manually updating numpy worked for me as well.

[nnansters]

Thanks for the extra information all. We'll try to include a fix in the next release, coming next week!

[nnansters]

Looks like the required numpy version (>=1.14.0) might be wrong. It seems like it should be closer to >=1.21.0 (at least based on the current nannyml code).

I reproduced it as follows:

conda create --name nannyml python=3.8
conda activate nannyml
pip install numpy==1.19.5
pip install pandas==1.4.3
pip install nannyml==0.5.0
pip install IPython==8.4.0
# quickstart.py contains the code above
python quickstart.py  # data type 'string' not understood

These libraries all seem to be compatible with one another (otherwise pip would show a warning or automatically update libraries to compatible versions, neither of which happened).

I fixed it by updating numpy:

pip install numpy==1.21.0
python quickstart.py  # works

I can also fix the issue by just explicitly converting each column to string:

...
display(ranked_features)

for c in reference.columns:
    reference[c] = reference[c].astype("string")

for c in analysis.columns:
    analysis[c] = analysis[c].astype("string")

calc = nml.StatisticalOutputDriftCalculator(
...

I'm running Ubuntu 22, in case it matters.

Sorry for missing this Bernhard, I was out on holiday. Thanks for the report and the investigation!

[nikml]

Nice dig down!

On the latest version, 0.5.3, with this code sample:

import pandas as pd
import nannyml as nml
from IPython.display import display

# Load synthetic data
reference, analysis, analysis_target = nml.load_synthetic_binary_classification_dataset()
display(reference.head())
display(analysis.head())

# Choose a chunker or set a chunk size
chunk_size = 5000

# initialize, specify required data columns, fit estimator and estimate
estimator = nml.CBPE(
   y_pred_proba='y_pred_proba',
   y_pred='y_pred',
   y_true='work_home_actual',
   timestamp_column_name='timestamp',
   metrics=['roc_auc'],
   chunk_size=chunk_size,
   # problem_type='classification_binary',
)
estimator = estimator.fit(reference)
estimated_performance = estimator.estimate(analysis)

# Show results
figure = estimated_performance.plot(kind='performance', metric='roc_auc', plot_reference=True)
figure.show()

# Define feature columns
feature_column_names = [
    col for col in reference.columns if col not in [
        'timestamp', 'y_pred_proba', 'period', 'y_pred', 'work_home_actual', 'identifier'
    ]]

# Let's initialize the object that will perform the Univariate Drift calculations
univariate_calculator = nml.UnivariateStatisticalDriftCalculator(
    feature_column_names=feature_column_names,
    timestamp_column_name='timestamp',
    chunk_size=chunk_size
)
univariate_calculator = univariate_calculator.fit(reference)
univariate_results = univariate_calculator.calculate(analysis)
# Plot drift results for all model inputs
for feature in univariate_calculator.feature_column_names:
    figure = univariate_results.plot(
        kind='feature_drift',
        metric='statistic',
        feature_column_name=feature,
        plot_reference=True
    )
    figure.show()

# Rank features based on number of alerts
ranker = nml.Ranker.by('alert_count')
ranked_features = ranker.rank(univariate_results, only_drifting = False)
display(ranked_features)

calc = nml.StatisticalOutputDriftCalculator(
    y_pred='y_pred',
    y_pred_proba='y_pred_proba',
    timestamp_column_name='timestamp',
    # problem_type='classification_binary'
)
calc.fit(reference)
results = calc.calculate(analysis)

figure = results.plot(kind='prediction_drift', plot_reference=True)
figure.show()

# Let's initialize the object that will perform Data Reconstruction with PCA
rcerror_calculator = nml.DataReconstructionDriftCalculator(feature_column_names=feature_column_names, timestamp_column_name='timestamp', chunk_size=chunk_size).fit(reference_data=reference)
# let's see Reconstruction error statistics for all available data
rcerror_results = rcerror_calculator.calculate(analysis)
figure = rcerror_results.plot(kind='drift', plot_reference=True)
figure.show()

the issue is not reproducable on my end with numpy 1.22.4 and pandas 1.4.3

[stale]

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