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+--- Thread: Error in if-then-else python code (/thread-37835.html)
Error in if-then-else python code - Led_Zeppelin - Jul-27-2022
When I run the program with the following final code, I get the error shown.
Error:ValueError Traceback (most recent call last)
Input In [34], in <cell line: 2>()
1 model = ExtraTreesClassifier()
----> 2 model.fit(X, y)
3 print(model.feature_importances_)
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\ensemble\_forest.py:331, in BaseForest.fit(self, X, y, sample_weight)
329 if issparse(y):
330 raise ValueError("sparse multilabel-indicator for y is not supported.")
--> 331 X, y = self._validate_data(
332 X, y, multi_output=True, accept_sparse="csc", dtype=DTYPE
333 )
334 if sample_weight is not None:
335 sample_weight = _check_sample_weight(sample_weight, X)
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\base.py:596, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
594 y = check_array(y, input_name="y", **check_y_params)
595 else:
--> 596 X, y = check_X_y(X, y, **check_params)
597 out = X, y
599 if not no_val_X and check_params.get("ensure_2d", True):
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:1090, 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)
1070 raise ValueError(
1071 f"{estimator_name} requires y to be passed, but the target y is None"
1072 )
1074 X = check_array(
1075 X,
1076 accept_sparse=accept_sparse,
(...)
1087 input_name="X",
1088 )
-> 1090 y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
1092 check_consistent_length(X, y)
1094 return X, y
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:1100, in _check_y(y, multi_output, y_numeric, estimator)
1098 """Isolated part of check_X_y dedicated to y validation"""
1099 if multi_output:
-> 1100 y = check_array(
1101 y,
1102 accept_sparse="csr",
1103 force_all_finite=True,
1104 ensure_2d=False,
1105 dtype=None,
1106 input_name="y",
1107 estimator=estimator,
1108 )
1109 else:
1110 estimator_name = _check_estimator_name(estimator)
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:899, 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, input_name)
893 raise ValueError(
894 "Found array with dim %d. %s expected <= 2."
895 % (array.ndim, estimator_name)
896 )
898 if force_all_finite:
--> 899 _assert_all_finite(
900 array,
901 input_name=input_name,
902 estimator_name=estimator_name,
903 allow_nan=force_all_finite == "allow-nan",
904 )
906 if ensure_min_samples > 0:
907 n_samples = _num_samples(array)
File ~\miniconda3\envs\pump-failure-pred\lib\site-packages\sklearn\utils\validation.py:146, in _assert_all_finite(X, allow_nan, msg_dtype, estimator_name, input_name)
124 if (
125 not allow_nan
126 and estimator_name
(...)
130 # Improve the error message on how to handle missing values in
131 # scikit-learn.
132 msg_err += (
133 f"\n{estimator_name} does not accept missing values"
134 " encoded as NaN natively. For supervised learning, you might want"
(...)
144 "#estimators-that-handle-nan-values"
145 )
--> 146 raise ValueError(msg_err)
148 # for object dtype data, we only check for NaNs (GH-13254)
149 elif X.dtype == np.dtype("object") and not allow_nan:
ValueError: Input y contains NaN.df2["machine_status"] = df2["machine_status"].map(
lambda x: 0
if x == "NORMAL"
else 1
if x == "BROKEN"
else 2
if x == "BROKEN"
else np.NaNNow machine status is either normal, recovering or broken. But I combine Recovering with Broken. ...I think I know the answer to this so please be patient and I will check if my idea works.
RE: Error in if-then-else python code - Led_Zeppelin - Jul-27-2022
I have the answer to this question. Thus, there is no need to answer.
Respectfully,
LZ
RE: Error in if-then-else python code - deanhystad - Jul-27-2022
If you have the answer you should post the answer.
How was BROKEN(1) different from BROKEN(2)? This works for me, but I never get 2 as a status code for obvious reasons.
import pandas as pd
import numpy as np
df = pd.DataFrame({"status": ["NORMAL", "BROKEN", "BROKEN", "UNKNOWN"]})
df["code"] = df["status"].map(
lambda x:
0 if x == "NORMAL" else
1 if x == "BROKEN" else
np.nan
)
print(df)Output:0 NORMAL 0.0
1 BROKEN 1.0
2 BROKEN 1.0
3 UNKNOWN NaNRE: Error in if-then-else python code - Led_Zeppelin - Jul-27-2022
Here is the original if-then-else statement
df2["machine_status"] = df2["machine_status"].map(
lambda x: 0
if x == "NORMAL"
else 1
if x == "BROKEN"
else 2
if x == "BROKEN"
else np.NaNI am changing strings such a NORMAL, BROKEN or RECOVERING to numbers. The numbers are O if NORMAL and 1 if BROKEN or RECOVERING.At first, I thought that the unexpected Error (the interpreter said my machine-status columns had NANs and then it failed. i thought it was confusing a 0 as a number for 0 as a symbol. I did have a lot of NAN's. I did not know what o do about this. That was not the case.
I fact the if-then-else statement was clearly wrong.
So, I corrected the if-then-else statement to this:
df2["machine_status"] = df2["machine_status"].map(
lambda x: 0
if x == "NORMAL"
else 1
)And then my program worked as I wanted.Respectfully,
LZ
RE: Error in if-then-else python code - deanhystad - Jul-27-2022
So the error is that fit(x, y) tosses an error if you have NaN's in x or y, and your lambda to convert machine status to numbers was inserting NaN's.
RE: Error in if-then-else python code - Led_Zeppelin - Jul-27-2022
Yes, ii was. My initial guess was way off the mark.
Way off.
R,
LZ
RE: Error in if-then-else python code - deanhystad - Jul-27-2022
I think you would get a lot of benefit from putting more effort into your posts. When I post I try to write a small example that other people can run but still demonstrates my problem. Often writing the example exposes the solution, and if nothing else demonstrating to others that I've really put some work into solving this problem and making it easy for them to help me will get me good answers quicker. Not to mention that writing small code examples for a specific purpose makes me a better programmer and a better debugger.