我正在嘗試為系外行星目錄中的資料撰寫決策樹方法。這是我的碩士課程之一的作業室。我在 Jupyter Notebook 中寫過這個
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
data = pd.read_csv('exoplanet.eu_catalog_2021.12.15.csv')
data_new = data.select_dtypes(include=['float64'])#Select only dtype float64 data
data_new[~data_new.isin([np.nan, np.inf, -np.inf]).any(1)]
data_new_2 = data_new.loc[:,('mass', 'mass_error_min')]
data_new_2.dropna(subset =["mass_error_min"], inplace = True)
data_new_2.info()
print(data_new_2)
有了這個結果
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1425 entries, 1 to 4892
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 mass 1425 non-null float64
1 mass_error_min 1425 non-null float64
dtypes: float64(2)
memory usage: 33.4 KB
如您所見，沒有空單元格。此外，我寫這個是為了將所有數字轉換為 float64（以防萬一！）
data_new_2['mass'] = data_new_2['mass'].astype(float)
data_new_2['mass_error_min'] = data_new_2['mass_error_min'].astype(float)
然后，我將資料拆分為訓練和測驗子集
from sklearn.model_selection import train_test_split
X = data_new_2.drop(["mass"], axis = 1)
y = data_new_2["mass"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state = 42)
沒有問題......直到這部分
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier.fit(X_train, y_train_2)
因為我收到此錯誤訊息
ValueError Traceback (most recent call last)
<ipython-input-327-7b81afce3234> in <module>
1 from sklearn.tree import DecisionTreeClassifier
2 classifier = DecisionTreeClassifier()
----> 3 classifier.fit(X_train, y_train_2)
.
.
.
~/.local/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
104 msg_err.format
105 (type_err,
--> 106 msg_dtype if msg_dtype is not None else X.dtype)
107 )
108 # for object dtype data, we only check for NaNs (GH-13254)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
我不明白為什么會出現此錯誤訊息，因為我在 X_train 和 y_train 資料中沒有 Nan、infitnity 或“太大”資料。
我能做什么？
uj5u.com熱心網友回復：
您的mass_error_min列中有一些無限值：
data_new_2.describe()
mass mass_error_min
count 1425.000000 1425.0000
mean 6.060956 inf
std 13.568726 NaN
min 0.000002 0.0000
25% 0.054750 0.0116
50% 0.725000 0.0700
75% 3.213000 0.5300
max 135.300000 inf
所以，你必須用一些值填充那些 inf，使用以下代碼：
value = data_new_2['mass_error_min'].quantile(0.98)
data_new_2 = data_new_2.replace(np.inf, value)
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