We Keep Coding

ValueError: Input contains NaN, infinity or a value too large for dtype('float64') While fitting the model After Imputation

I pass the predictors from an imputation pipeline,
I check the columns for NaN and inf values with
col_name = X.columns.to_series()[np.isinf(X).any()]
There are no columns with missing values or inf values.
classifier = MLPClassifier
I do: clf.fit(X,y) then get the error ValueError: Input contains NaN, infinity or a value too large for dtype('float64')
What am I missing here? Can anybody help or guide me as to what to do????
What might be the other possible explanations for this error? What else should I be checking for? Also
full traceback:
Input In [137], in <cell line: 4>()
1 #l = list(X.isin([np.inf, -np.inf]))
2 #col_name = X.columns.to_series()[np.isnan(X).any()]
3 #col_name
----> 4 clf.fit(X, y)
File ~\anaconda3\lib\site-packages\sklearn\pipeline.py:394, in Pipeline.fit(self, X, y, **fit_params)
392 if self._final_estimator != "passthrough":
393 fit_params_last_step = fit_params_steps[self.steps[-1][0]]
--> 394 self._final_estimator.fit(Xt, y, **fit_params_last_step)
396 return self
File ~\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:752, in BaseMultilayerPerceptron.fit(self, X, y)
735 def fit(self, X, y):
736 """Fit the model to data matrix X and target(s) y.
737
738 Parameters
(...)
750 Returns a trained MLP model.
751 """
--> 752 return self._fit(X, y, incremental=False)
File ~\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:393, in BaseMultilayerPerceptron._fit(self, X, y, incremental)
386 raise ValueError(
387 "hidden_layer_sizes must be > 0, got %s." % hidden_layer_sizes
388 )
389 first_pass = not hasattr(self, "coefs_") or (
390 not self.warm_start and not incremental
391 )
--> 393 X, y = self._validate_input(X, y, incremental, reset=first_pass)
395 n_samples, n_features = X.shape
397 # Ensure y is 2D
File ~\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:1100, in MLPClassifier._validate_input(self, X, y, incremental, reset)
1099 def _validate_input(self, X, y, incremental, reset):
-> 1100 X, y = self._validate_data(
1101 X,
1102 y,
1103 accept_sparse=["csr", "csc"],
1104 multi_output=True,
1105 dtype=(np.float64, np.float32),
1106 reset=reset,
1107 )
1108 if y.ndim == 2 and y.shape[1] == 1:
1109 y = column_or_1d(y, warn=True)
File ~\anaconda3\lib\site-packages\sklearn\base.py:581, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
579 y = check_array(y, **check_y_params)
580 else:
--> 581 X, y = check_X_y(X, y, **check_params)
582 out = X, y
584 if not no_val_X and check_params.get("ensure_2d", True):
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:979, in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
962 raise ValueError("y cannot be None")
964 X = check_array(
965 X,
966 accept_sparse=accept_sparse,
(...)
976 estimator=estimator,
977 )
--> 979 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric)
981 check_consistent_length(X, y)
983 return X, y
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:989, in _check_y(y, multi_output, y_numeric)
987 """Isolated part of check_X_y dedicated to y validation"""
988 if multi_output:
--> 989 y = check_array(
990 y, accept_sparse="csr", force_all_finite=True, ensure_2d=False, dtype=None
991 )
992 else:
993 y = column_or_1d(y, warn=True)
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:800, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
794 raise ValueError(
795 "Found array with dim %d. %s expected <= 2."
796 % (array.ndim, estimator_name)
797 )
799 if force_all_finite:
--> 800 _assert_all_finite(array, allow_nan=force_all_finite == "allow-nan")
802 if ensure_min_samples > 0:
803 n_samples = _num_samples(array)
File ~\anaconda3\lib\site-packages\sklearn\utils\validation.py:114, in _assert_all_finite(X, allow_nan, msg_dtype)
107 if (
108 allow_nan
109 and np.isinf(X).any()
110 or not allow_nan
111 and not np.isfinite(X).all()
112 ):
113 type_err = "infinity" if allow_nan else "NaN, infinity"
--> 114 raise ValueError(
115 msg_err.format(
116 type_err, msg_dtype if msg_dtype is not None else X.dtype
117 )
118 )
119 # for object dtype data, we only check for NaNs (GH-13254)
120 elif X.dtype == np.dtype("object") and not allow_nan:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

You may want to check nan as well
col_name = X.columns.to_series()[np.isinf(X).any() | np.isnan(X).any()]

Python "sklearn" ValueError

hope you are having a great day! I know the problem is silly and most of you can probably figure it out. But i do need help. So yeah. Here's the problem:
The code goes something like this:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
data = pd.read_csv("Data.csv")
X = data.drop(columns = "Answers")
Y = data["Answers"]
algorithm = DecisionTreeClassifier()
algorithm.fit(X, Y)
And I know I'm dumb and probably don't know why its outputting this shi*:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-46-0deefc30b34f> in <module>
9 algorithm = DecisionTreeClassifier()
10
---> 11 algorithm.fit(X, Y)
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
888 """
889
--> 890 super().fit(
891 X, y,
892 sample_weight=sample_weight,
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
154 check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
155 check_y_params = dict(ensure_2d=False, dtype=None)
--> 156 X, y = self._validate_data(X, y,
157 validate_separately=(check_X_params,
158 check_y_params))
~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
427 # :(
428 check_X_params, check_y_params = validate_separately
--> 429 X = check_array(X, **check_X_params)
430 y = check_array(y, **check_y_params)
431 else:
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72 return f(**kwargs)
73 return inner_f
74
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
596 array = array.astype(dtype, casting="unsafe", copy=False)
597 else:
--> 598 array = np.asarray(array, order=order, dtype=dtype)
599 except ComplexWarning:
600 raise ValueError("Complex data not supported\n"
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
~\anaconda3\lib\site-packages\pandas\core\generic.py in __array__(self, dtype)
1779
1780 def __array__(self, dtype=None) -> np.ndarray:
-> 1781 return np.asarray(self._values, dtype=dtype)
1782
1783 def __array_wrap__(self, result, context=None):
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
ValueError: could not convert string to float: "Wat's your name?"
Thank you for viewing this problem and perhaps in solving it. Have a great day!

Passing a 2-d array as an element to sklearn.SVM

I have a single independent variable X where each element is a 2-d array with shape (20, 431). The variable X itself is a 2-d numpy array of shape (200,). . How do I pass this to the sklearn.SVM object?
Edit: actual dataframe:
Category Features
0 1 [[-177.08171, -219.89174, -253.55954, -218.560...
1 0 [[-291.89288, -316.40735, -389.8398, -413.6302...
2 1 [[-355.88293, -351.0909, -364.43524, -400.7097.
Each element of Features is a 20*431 numpy array. I need to use these features to classify the category.
x = data.iloc[:, 1].values
y = data.iloc[:, 0].values
x.shape
(200, )
x[0].shape
(20, 431)
y.shape
(200, )
Fitting the model after splitting into train and test data:
classifier = SVC(kernel = 'rbf', random_state=0)
classifier.fit(x_train, y_train)
Error:
TypeError Traceback (most recent call last)
TypeError: only size-1 arrays can be converted to Python scalars
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-203-fdf0fc8db087> in <module>
----> 1 classifier.fit(x_train, y_train)
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\svm\_base.py in fit(self, X, y, sample_weight)
160 X, y = self._validate_data(X, y, dtype=np.float64,
161 order='C', accept_sparse='csr',
--> 162 accept_large_sparse=False)
163
164 y = self._validate_targets(y)
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
430 y = check_array(y, **check_y_params)
431 else:
--> 432 X, y = check_X_y(X, y, **check_params)
433 out = X, y
434
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
71 FutureWarning)
72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
801 ensure_min_samples=ensure_min_samples,
802 ensure_min_features=ensure_min_features,
--> 803 estimator=estimator)
804 if multi_output:
805 y = check_array(y, accept_sparse='csr', force_all_finite=True,
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
71 FutureWarning)
72 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
597 array = array.astype(dtype, casting="unsafe", copy=False)
598 else:
--> 599 array = np.asarray(array, order=order, dtype=dtype)
600 except ComplexWarning:
601 raise ValueError("Complex data not supported\n"
~\Anaconda3\envs\LangDetEnv1.0\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order)
81
82 """
---> 83 return array(a, dtype, copy=False, order=order)
84
85
ValueError: setting an array element with a sequence.

ValueError: could not convert string to float SMOTE fit_sample Python Oversampling

I have a credit risk analysis dataset which goes like this:
Loan_ID Age Income(LPA) Employed_yr Education Loan_status
1 18 2.4 1 12th 1
2 46 43 26 Post Grad 0
3 22 12 4 Grad 0
4 25 17 1 Grad 1
1 means default and 0 means non-default in loan_status.
Now the number of defaults is very less around 1000, and number of non defaults are 25,000. So I want to do over sampling or synthetic sampling.
Till here the code runs fine
cred_loan = pd.read_csv("Credit_Risk_Analysis.csv")
from imblearn import under_sampling, over_sampling
from imblearn.over_sampling import SMOTE
y= cred_loan.loan_status
X = cred_loan.drop('loan_status', axis=1)
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,
random_state=27)
sm = SMOTE(random_state=27, ratio=1.0)
from sklearn.linear_model import LogisticRegression
After this I do the following and there is an error
[IN] X_train, y_train = sm.fit_sample(X_train, y_train)
[OUT]ValueError Traceback (most recent call
last)
<ipython-input-39-0995f82b5705> in <module>
----> 1 X_train, y_train = sm.fit_sample(X_train, y_train)
2
~\Anaconda3\lib\site-packages\imblearn\base.py in fit_resample(self, X, y)
77
78 check_classification_targets(y)
---> 79 X, y, binarize_y = self._check_X_y(X, y)
80
81 self.sampling_strategy_ = check_sampling_strategy(
~\Anaconda3\lib\site-packages\imblearn\base.py in _check_X_y(X, y)
135 def _check_X_y(X, y):
136 y, binarize_y = check_target_type(y,
indicate_one_vs_all=True)
--> 137 X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
138 return X, y, binarize_y
139
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y,
accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite,
ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features,
y_numeric, warn_on_dtype, estimator)
754 warnings.warn("A column-vector y was passed when a 1d
array was"
755 " expected. Please change the shape of y
to "
--> 756 "(n_samples, ), for example using
ravel().",
757 DataConversionWarning, stacklevel=2)
758 return np.ravel(y)
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in
check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy,
force_all_finite, ensure_2d, allow_nd, ensure_min_samples,
ensure_min_features, warn_on_dtype, estimator)
565 if copy and np.may_share_memory(array, array_orig):
566 array = np.array(array, dtype=dtype, order=order)
--> 567
568 if (warn_on_dtype and dtypes_orig is not None and
569 {array.dtype} != set(dtypes_orig)):
ValueError: could not convert string to float: 'MORTGAGE'
Can anyone help please?

Logistic Regression get Value error could not convert string to float: '?'

I am very new at this stuff. This is from a course I am taking;
I need to fit the Logistic Regression classifier
I enter
from sklearn.linear_model import LogisticRegression
C=1.0
classifier = LogisticRegression(C=C, penalty='l1')
classifier.fit(x, y)
and get a Value Error
ValueError Traceback (most recent call last) <ipython-input-33-9d4de811daf9> in <module>()
----> 1 classifier.fit(x, y)
~\Anaconda3\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight) 1214 1215 X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype,
-> 1216 order="C") 1217 check_classification_targets(y) 1218 self.classes_ = np.unique(y)
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
571 X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
572 ensure_2d, allow_nd, ensure_min_samples,
--> 573 ensure_min_features, warn_on_dtype, estimator)
574 if multi_output:
575 y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,
~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
431 force_all_finite)
432 else:
--> 433 array = np.array(array, dtype=dtype, order=order, copy=copy)
434
435 if ensure_2d:
ValueError: could not convert string to float: '?'
Please help

The training input x and output y must be of type np.float64.
If you want to use strings, you need to encode them before fitting .
Check this post out for it :
Link