Convert float to int not loosing information of original values-CodePudding

I would need to transform float to int. However, I would like to not loose any information while converting it. The values (from a dataframe column used a y in modeling build) that I am taking into account are as follows:

If I convert them into int directly, I might get -0.5 as 0 or -1, so I will loose some information.

I need to convert the values above to int because I need to pass them to fit a model model.fit(X, y). Any format that could allow me to pass these values in the fit function (the above column is meant y column)?

Code:

from sklearn.preprocessing import MinMaxScaler

le = preprocessing.LabelEncoder()
X = df[['Col1','Col2']].apply(le.fit_transform)
X_transformed=np.concatenate(((X[['Col1']]),(X[['Col2']])), axis=1)

y=df['Label'].values

scaler=MinMaxScaler()
X_scaled=scaler.fit_transform(X_transformed)

model_LS = LabelSpreading(kernel='knn', 
                          gamma=70,          
                          alpha=0.5, 
                          max_iter=30,
                          tol=0.001,
                          n_jobs=-1, 
                         )


LS=model_LS.fit(X_scaled, y)

Data:

Col1         Col2     Label
Cust1        Cust2     1.0
Cust1        Cust4     1.0
Cust4        Cust5     -1.5
Cust12       Cust6     9.0

The error that I am getting running the above code is:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-174-14429cc07d75> in <module>
      2 
----> 3 LS=model_LS.fit(X_scaled, y)

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/semi_supervised/_label_propagation.py in fit(self, X, y)
    228         X, y = self._validate_data(X, y)
    229         self.X_ = X
--> 230         check_classification_targets(y)
    231 
    232         # actual graph construction (implementations should override this)

~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/multiclass.py in check_classification_targets(y)
    181     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
    182                       'multilabel-indicator', 'multilabel-sequences']:
--> 183         raise ValueError("Unknown label type: %r" % y_type)
    184 
    185 

ValueError: Unknown label type: 'continuous'

CodePudding user response：

You can multiply your values to remove the decimal part:

df = pd.DataFrame({'Label': [1.0, -1.3, 0.75, 9.0, 7.8236]})
decimals = df['Label'].astype(str).str.split('.').str[1].str.len().max()

df['y'] = df['Label'].mul(float(f"1e{decimals}")).astype(int)
print(df)

# Output:
    Label      y
0  1.0000  10000
1 -1.3000 -13000
2  0.7500   7500
3  9.0000  90000
4  7.8236  78236

CodePudding user response：

I think you need:

from sklearn.preprocessing import LabelEncoder
df = pd.DataFrame(data={'y':[-1.0, 0.0 , 9.0, -0.5, 1.5 , 1.5]})

le = LabelEncoder()
le.fit(df['y'])
df['y'] = le.transform(df['y'])
print(df)

OUTPUT