TypeError: Object of type float32 is not JSON serializable

[nosacapital]

I ran a regression algorithm for LightGBM on the same data in the code below and had no problems in obtaining results. It ran in full with no hitches. I needed to do the same with Xgboost on the same data and get the error in the title. I tried casting the X_train to both float32 and float64 via numpy but got the same message. I even tried scaling and then casting and got the same thing. Please find the code, the output, and the error message below:

CODE

from supervised.automl import AutoML
import pandas as pd

# Load your data into X and y
# Load the dataset from a Parquet file
train_features = pd.read_parquet('X_train.parquet')

# Targets use for your supervised trainning
train_targets = pd.read_parquet('y_train.parquet')

# Merge train_features and train_targets for ease of use

train_data = pd.merge(train_features, train_targets, on=['id', 'Moons'], how='inner')


print(" Files downloaded and ready to go!")

# Features to be dropped
drop_features = ['id', 'Moons', 'target_w', 'target_r', 'target_g', 'target_b']

# Split the dataset into features and target variable
X = train_data.drop(columns=drop_features, axis=1)
y = train_data['target_r']

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

X_train_scaled = X_train_scaled.astype(np.float64)

automl = AutoML(
    eval_metric="auto",
    algorithms=["Xgboost"],
    total_time_limit=12*3600,
    model_time_limit=9000,
    explain_level=0,
    mode="Compete",
    ml_task="regression",
    features_selection=True,
    stack_models=True,
    kmeans_features=True,
    golden_features=True   
)
automl.fit(X_train, y_train)

OUTPUT

AutoML directory: AutoML_36
The task is regression with evaluation metric rmse
AutoML will use algorithms: ['Xgboost']
AutoML will stack models
AutoML will ensemble available models
[/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/sklearn/preprocessing/_data.py:239](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/sklearn/preprocessing/_data.py:239): UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.
[/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/sklearn/preprocessing/_data.py:239](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/sklearn/preprocessing/_data.py:239): UserWarning: Numerical issues were encountered when centering the data and might not be solved. Dataset may contain too large values. You may need to prescale your features.
AutoML steps: ['adjust_validation', 'simple_algorithms', 'default_algorithms', 'not_so_random', 'golden_features', 'kmeans_features', 'insert_random_feature', 'features_selection', 'hill_climbing_1', 'hill_climbing_2', 'boost_on_errors', 'ensemble', 'stack', 'ensemble_stacked']
* Step adjust_validation will try to check up to 1 model
1_DecisionTree rmse 0.183542 trained in 1.51 seconds
Adjust validation. Remove: 1_DecisionTree
Validation strategy: 10-fold CV Shuffle
Skip simple_algorithms because no parameters were generated.
* Step default_algorithms will try to check up to 1 model
1_Default_Xgboost rmse 0.134586 trained in 2008.23 seconds

ERROR
TypeError                                 Traceback (most recent call last)
[/var/folders/zz/kp45w8ds4rv3m5nhzcfkprf80000gn/T/ipykernel_4972/623880635.py](https://file+.vscode-resource.vscode-cdn.net/var/folders/zz/kp45w8ds4rv3m5nhzcfkprf80000gn/T/ipykernel_4972/623880635.py) in ()
     17 
     18 #X_train = X_train.astype(np.float64)
---> 19 automl.fit(X_train_scaled, y_train)

[/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/automl.py](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/automl.py) in fit(self, X, y, sample_weight, cv)
    366             AutoML object: Returns `self`
    367         """
--> 368         return self._fit(X, y, sample_weight, cv)
    369 
    370     def predict(self, X: Union[List, numpy.ndarray, pandas.DataFrame]) -> numpy.ndarray:

[/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/base_automl.py](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/base_automl.py) in _fit(self, X, y, sample_weight, cv)
   1132 
   1133         except Exception as e:
-> 1134             raise e
   1135 
   1136         return self

[/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/base_automl.py](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Caskroom/miniforge/base/envs/tensorflow/lib/python3.9/site-packages/supervised/base_automl.py) in _fit(self, X, y, sample_weight, cv)
   1118                         params["status"] = "error"
   1119 
-> 1120                     self.save_progress(step, generated_params)
...
--> 179         raise TypeError(f'Object of type {o.__class__.__name__} '
    180                         f'is not JSON serializable')
    181 

TypeError: Object of type float32 is not JSON serializable

As mentioned this is not the only solution, this happens to be the latest one. Please advise on how to solve the problem. Thanks

[pplonski]

Hi @nosacapital,

Thank you for reporting the issue. Could you send me full error output? Looks like some problem with saving model ...

[nosacapital]

Hello @pplonski
What you see is the full error. I am working in Visual Studio Code and on expanding the output into the text editor, I got the following gibberish which doesn't look useful:
"name": "TypeError",
"message": "Object of type float32 is not JSON serializable",
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}
Perhaps you can decipher something from this but as said this is gibberish to me. Looking forward to hearing from you

[pplonski]

Thank you! Do you have the same error in errors.md file in the results_path directory? Are you running AutoML in notebook?

[nosacapital]

I am running AutoML inside Visual Studio Code using the Jupyter notebook plugin. Let me look into the results_path and get back to you

[nosacapital]

I have just gone through the AutoML 36 folder and there is no errors.md nor results_path directory. I will look again but I have gone through every file and folder carefully to ensure I don't miss either.

[pplonski]

It should be fixed with #496 fix.