TypeError: 'SparseTensor' object is not subscriptable

[murlisingh]

I am getting this error while running the model.
I have downloaded Car Price Prediction data set from Kaggle.

https://www.kaggle.com/adityadesai13/used-car-dataset-ford-and-mercedes?select=vw.csv

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.model_selection import train_test_split

X = Car_Data.drop("price",axis=1)
y = Car_Data["price"]

ct = make_column_transformer((MinMaxScaler(),["mileage","tax","mpg","engineSize","year"]),
(OneHotEncoder(handle_unknown="ignore"),["model","transmission","fuelType"]))

X_train_1,X_test_1,y_train_1,y_test_1 = train_test_split(X,y,test_size=0.15,random_state=42)

ct.fit(X_train_1)
X_train_Car_Normalized = ct.transform(X_train_1)
X_test_Car_Normalized = ct.transform(X_test_1)
X_train_Car_Normalized

tf.random.set_seed

car_model_Normalized = tf.keras.Sequential([
tf.keras.layers.Dense(20,activation='relu'),
tf.keras.layers.Dense(80,activation='relu'),
tf.keras.layers.Dense(1)
])
car_model_Normalized.compile(loss = tf.keras.losses.mae,optimizer = tf.keras.optimizers.Adam(lr=0.1),metrics=["mae"])
car_model_Normalized.fit(X_train_Car_Normalized,y_train_1,epochs = 200)

WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.
Epoch 1/200

InvalidArgumentError Traceback (most recent call last)
in ()
7 ])
8 car_model_Normalized.compile(loss = tf.keras.losses.mae,optimizer = tf.keras.optimizers.Adam(lr=0.1),metrics=["mae"])
----> 9 car_model_Normalized.fit(X_train_Car_Normalized,y_train_1,epochs = 200)

6 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:

InvalidArgumentError: TypeError: 'SparseTensor' object is not subscriptable
Traceback (most recent call last):

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/script_ops.py", line 247, in call
return func(device, token, args)

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/script_ops.py", line 135, in call
ret = self._func(*args)

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py", line 620, in wrapper
return func(*args, **kwargs)

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py", line 504, in py_method
return [slice_array(inp) for inp in flat_inputs]

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py", line 504, in
return [slice_array(inp) for inp in flat_inputs]

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py", line 503, in slice_array
contiguous=contiguous)

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 53, in slice_arrays
entries = [[x[i:i + 1] for i in indices] for x in arrays]

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 53, in
entries = [[x[i:i + 1] for i in indices] for x in arrays]

File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 53, in
entries = [[x[i:i + 1] for i in indices] for x in arrays]

TypeError: 'SparseTensor' object is not subscriptable

 [[{{node EagerPyFunc}}]]
 [[IteratorGetNext]] [Op:__inference_train_function_109251]

Function call stack:
train_function

[mrdbourke]

Hey @murlisingh,
Looks like your model is having trouble fitting on a sparse tensor.
This probably starts with a sparse array created by make_column_transformer or OneHotEncoder.
I'd look into the following parameters:

• sparse_threshold in https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html
• sparse=True (try setting this to False) in https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
  In essence, you want to make sure once your data is encoded it's not in the form of a sparse array/tensor.
  To verify this, I'd print the outputs of your data preprocessing methods and make sure it isn't sparse before passing it to your model.