Fixing NumPy Error In Eurybia's Chisq_test

by Admin 43 views
Fixing NumPy Error in Eurybia's chisq_test

Hey everyone! Let's dive into a common issue you might encounter while using Eurybia, specifically when dealing with the chisq_test function within the SmartDrift module. This article will guide you through understanding the error, reproducing it, and hopefully, getting it fixed.

Understanding the Issue

So, you're using Eurybia's SmartDrift to detect data drift, and everything seems fine until you hit this wall: a TypeError in the chisq_test function. This usually happens when one of your string columns contains null values (represented as None or NaN). The traceback you see might look something like this:

TypeError: '<' not supported between instances of 'str' and 'float'

Essentially, NumPy's unique function, which is used internally by chisq_test, struggles when it encounters a mix of strings and floating-point NaN values in the same column. This is because it tries to sort the array, and you can't directly compare strings and floats.

This issue arises because the chisq_test function doesn't handle mixed data types (strings and NaNs) gracefully. When a column contains both strings and NaN values, NumPy's np.unique function fails during the sorting process, leading to the TypeError. Specifically, the error occurs in the _unique1d function within NumPy's arraysetops.py file, where it attempts to sort an array containing both strings and floating-point NaN values.

To elaborate, the core problem lies in how NumPy's unique function handles arrays with mixed data types. When np.unique is called on an array containing both strings and NaN values, it attempts to sort the array to identify unique elements. However, the default sorting algorithm in NumPy cannot compare strings and floating-point numbers directly, resulting in a TypeError. This is because the < operator is not defined for comparisons between string and float instances. The error message TypeError: '<' not supported between instances of 'str' and 'float' clearly indicates this incompatibility.

Furthermore, this issue is exacerbated by the presence of NaN values, which are often introduced when dealing with missing or null data in datasets. These NaN values are typically represented as floating-point numbers, which further complicates the sorting process when mixed with string data. The chisq_test function, being a statistical test designed for categorical data, expects consistent data types within each column. When it encounters mixed data types, it fails to process the data correctly, leading to the observed error.

Understanding this underlying cause is crucial for developing effective solutions. One approach is to preprocess the data to ensure consistent data types within each column before applying the chisq_test function. This might involve either converting all values in a column to strings or imputing NaN values with a suitable placeholder value that is compatible with the existing string data. By addressing this data type inconsistency, the chisq_test function can then be executed without encountering the TypeError.

Reproducing the Error

Let's get our hands dirty and reproduce this error. The following code snippet, inspired by the tutorial01-datadrift-over-years.ipynb tutorial, should do the trick:

import pandas as pd
from category_encoders import OrdinalEncoder
from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split

from eurybia import SmartDrift
from eurybia.data.data_loader import data_loading

house_df, house_dict = data_loading("house_prices")
house_df_learning = house_df.loc[house_df["YrSold"] == 2006]
house_df_2007 = house_df.loc[house_df["YrSold"] == 2007]
y_df_learning = house_df_learning["SalePrice"].to_frame()
X_df_learning = house_df_learning[house_df_learning.columns.difference(["SalePrice", "YrSold"])]
y_df_2007 = house_df_2007["SalePrice"].to_frame()
X_df_2007 = house_df_2007[house_df_2007.columns.difference(["SalePrice", "YrSold"])]

categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == "object"]
encoder = OrdinalEncoder(cols=categorical_features, handle_unknown="ignore", return_df=True).fit(X_df_learning)
X_df_learning_encoded = encoder.transform(X_df_learning)

Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)

regressor = LGBMRegressor(n_estimators=200).fit(Xtrain, ytrain)

print("cols", [(c, type(c)) for c in pd.unique(X_df_2007["MasVnrType"])])
SmartDrift(
    df_current=X_df_2007,
    df_baseline=X_df_learning,
    deployed_model=regressor,
    encoding=encoder,
).compile()

Make sure you have Eurybia and its dependencies installed. When you run this code, you should see the TypeError pop up.

Diving Deeper into the Code

Let's break down the code snippet to understand what's happening. First, we load the house_prices dataset using data_loading from Eurybia. We then split the data into two sets based on the YrSold column, specifically for the years 2006 and 2007. These datasets are assigned to house_df_learning and house_df_2007, respectively.

Next, we prepare the feature and target variables for both datasets. We exclude the SalePrice and YrSold columns from the feature sets (X_df_learning and X_df_2007). The target variables (y_df_learning and y_df_2007) are the SalePrice column.

Categorical features are identified based on their data type (object). An OrdinalEncoder from the category_encoders library is used to encode these categorical features. The encoder is fitted on the X_df_learning dataset and then transforms it into X_df_learning_encoded.

A LightGBM regressor (LGBMRegressor) is trained using the encoded training data. The data is split into training and testing sets using train_test_split from scikit-learn, with a 75% training size and a random state for reproducibility.

Finally, the SmartDrift object is instantiated with the current and baseline datasets, the trained regressor, and the encoder. The compile method is then called, which triggers the chisq_test function internally. The error occurs when chisq_test encounters a column in X_df_2007 (specifically, MasVnrType) that contains both string values and NaN values. The print statement before the SmartDrift instantiation confirms that MasVnrType contains NaN values along with other string values.

Possible Solutions and Workarounds

Okay, so how do we fix this mess? Here are a few approaches:

  1. Data Cleaning: The most robust solution is to clean your data. Identify columns with mixed types and either fill the NaN values with a suitable string (like "Missing") or remove rows with NaN values in those columns. Be careful when removing rows, as it can introduce bias.

  2. Type Conversion: Convert the entire column to a string type. This way, NaN values will be represented as strings (e.g., "nan"), and NumPy won't complain. You can do this using df['column_name'] = df['column_name'].astype(str).

  3. Custom chisq_test: If you can't modify the data, you might need to write your own version of chisq_test that handles mixed types gracefully. This is more advanced but gives you full control.

Let's look at an example of the type conversion approach:

import pandas as pd
from category_encoders import OrdinalEncoder
from lightgbm import LGBMRegressor
from sklearn.model_selection import train_test_split

from eurybia import SmartDrift
from eurybia.data.data_loader import data_loading

house_df, house_dict = data_loading("house_prices")
house_df_learning = house_df.loc[house_df["YrSold"] == 2006]
house_df_2007 = house_df.loc[house_df["YrSold"] == 2007]
y_df_learning = house_df_learning["SalePrice"].to_frame()
X_df_learning = house_df_learning[house_df_learning.columns.difference(["SalePrice", "YrSold"])]
y_df_2007 = house_df_2007["SalePrice"].to_frame()
X_df_2007 = house_df_2007[house_df_2007.columns.difference(["SalePrice", "YrSold"])]

# Convert 'MasVnrType' column to string
X_df_2007['MasVnrType'] = X_df_2007['MasVnrType'].astype(str)

categorical_features = [col for col in X_df_learning.columns if X_df_learning[col].dtype == "object"]
encoder = OrdinalEncoder(cols=categorical_features, handle_unknown="ignore", return_df=True).fit(X_df_learning)
X_df_learning_encoded = encoder.transform(X_df_learning)

Xtrain, Xtest, ytrain, ytest = train_test_split(X_df_learning_encoded, y_df_learning, train_size=0.75, random_state=1)

regressor = LGBMRegressor(n_estimators=200).fit(Xtrain, ytrain)

print("cols", [(c, type(c)) for c in pd.unique(X_df_2007["MasVnrType"])])
SmartDrift(
    df_current=X_df_2007,
    df_baseline=X_df_learning,
    deployed_model=regressor,
    encoding=encoder,
).compile()

By converting the MasVnrType column to a string type before calling SmartDrift, you sidestep the TypeError. This ensures that all values in the column are strings, including the NaN values (which will be represented as "nan"), allowing NumPy to sort the array without issues.

Reporting and Contributing

If you encounter this issue, consider reporting it to the Eurybia developers. Providing a minimal reproducible example (like the one above) helps them understand and fix the problem. Also, if you're feeling adventurous, you could contribute a fix yourself!

Conclusion

Dealing with data drift can be tricky, and encountering errors like this is part of the process. By understanding the root cause and applying appropriate workarounds, you can keep your Eurybia workflows running smoothly. Keep experimenting, keep learning, and happy drifting!