I am trying to use sklearn to train a decision tree based on my dataset.
When I was trying to slicing the data to (outcome:Y, and predicting variables:X), it turns out that the outcome (my label) is in True/False:
#data slicing
X = df.values[:,3:27] #X are the sets of predicting variable, dropping unique_id and student name here
Y = df.values[:,'OffTask'] #Y is our predicted value (outcome), it is in the 3rd column
This is how I do, but I do not know whether this is the right approach:
#convert the label "OffTask" to dummy
df1 = pd.get_dummies(df,columns=["OffTask"])
df1
My trouble is the dataset df1 return my label Offtask to OffTask_N and OffTask_Y
Can someone know how to fix it?
get_dummies is used for converting nominal string values to integer. It returns as many as column as many unique string values are available in columns eg:
df={'color':['red','green','blue'],'price':[1200,3000,2500]}
my_df=pd.DataFrame(df)
pd.get_dummies(my_df)
In your case you can drop first value, wherever value is null can be considered it will be first value
You could make the pd.get_dummies to return only one column by setting drop_first=True
y = pd.get_dummies(df,columns=["OffTask"], drop_first=True)
But this is not the recommended way to convert the label to binaries. I would suggest using labelbinarizer for this purpose.
Example:
from sklearn import preprocessing
lb = preprocessing.LabelBinarizer()
lb.fit_transform(pd.DataFrame({'OffTask':['yes', 'no', 'no', 'yes']}))
#
array([[1],
[0],
[0],
[1]])
My data has a feature called level, and the data may have levels(-1,0,1,2,3) but my data now has only 2 levels 0 and -1. I'm using python for binary classification. How to do one-hot-encoding with all levels? What is the right approach to deal with this problem? Can I include all levels as I may expect them in test data? Or should I use only 2 levels ?
Let me rewrite it in this block.
Levels in train data:
(-1, 0)
Levels in test data:
(-1, 0, 1, 2, 3)
I think that the best way will be:
create 2 columns for train data (1st column will have 1 where value == -1 and 0 otherwise; 2nd column will have 1 where value == 0 and 0 otherwise)
group (1, 2, 3) in test data
do the same encoding as in the first bullet but for group you will have 0 in the first column and 0 in the second column
So you will have 2 levels in general (for value == -1 and value == 0) and 3rd level where all values in one-hot-encoded vector will be equal to zero.
If X=[-1,0,1,2,3], for onehot you can try:
from sklearn.preprocessing import OneHotEncoder
one_hot_enc=OneHotEncoder()
X_onehot=one_hot_enc.fit_transform(X.reshape(-1,1)) #reshape to convert 1-d array to 2-d array
print(X_onehot.toarray())
Since you have negative values like -1 in your class, trying one_hot will throw
ValueError: X needs to contain only non-negative integers.
for this you can first try Label-encoding first:
from sklearn.preprocessing import LabelEncoder
enc=LabelEncoder()
x_new=enc.fit_transform(X)
print(x_new.classes_)
feed x_new in place of X to OneHotEncoder
I have a series like:
df['ID'] = ['ABC123', 'IDF345', ...]
I'm using scikit's LabelEncoder to convert it to numerical values to be fed into the RandomForestClassifier.
During the training, I'm doing as follows:
le_id = LabelEncoder()
df['ID'] = le_id.fit_transform(df.ID)
But, now for testing/prediction, when I pass in new data, I want to transform the 'ID' from this data based on le_id i.e., if same values are present then transform it according to the above label encoder, otherwise assign a new numerical value.
In the test file, I was doing as follows:
new_df['ID'] = le_dpid.transform(new_df.ID)
But, I'm getting the following error: ValueError: y contains new labels
How do I fix this?? Thanks!
UPDATE:
So the task I have is to use the below (for example) as training data and predict the 'High', 'Mod', 'Low' values for new BankNum, ID combinations. The model should learn the characteristics where a 'High' is given, where a 'Low' is given from the training dataset. For example, below a 'High' is given when there are multiple entries with same BankNum and different IDs.
df =
BankNum | ID | Labels
0098-7772 | AB123 | High
0098-7772 | ED245 | High
0098-7772 | ED343 | High
0870-7771 | ED200 | Mod
0870-7771 | ED100 | Mod
0098-2123 | GH564 | Low
And then predict it on something like:
BankNum | ID |
00982222 | AB999 |
00982222 | AB999 |
00981111 | AB890 |
I'm doing something like this:
df['BankNum'] = df.BankNum.astype(np.float128)
le_id = LabelEncoder()
df['ID'] = le_id.fit_transform(df.ID)
X_train, X_test, y_train, y_test = train_test_split(df[['BankNum', 'ID'], df.Labels, test_size=0.25, random_state=42)
clf = RandomForestClassifier(random_state=42, n_estimators=140)
clf.fit(X_train, y_train)
I think the error message is very clear: Your test dataset contains ID labels which have not been included in your training data set. For this items, the LabelEncoder can not find a suitable numeric value to represent. There are a few ways to solve this problem. You can either try to balance your data set, so that you are sure that each label is not only present in your test but also in your training data. Otherwise, you can try to follow one of the ideas presented here.
One of the possibles solutions is, that you search through your data set at the beginning, get a list of all unique ID values, train the LabelEncoder on this list, and keep the rest of your code just as it is at the moment.
An other possible solution is, to check that the test data have only labels which have been seen in the training process. If there is a new label, you have to set it to some fallback value like unknown_id (or something like this). Doin this, you put all new, unknown IDs in one class; for this items the prediction will then fail, but you can use the rest of your code as it is now.
you can try solution from "sklearn.LabelEncoder with never seen before values" https://stackoverflow.com/a/48169252/9043549
The thing is to create dictionary with classes, than map column and fill new classes with some "known value"
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
suf="_le"
col="a"
df[col+suf] = le.fit_transform(df[col])
dic = dict(zip(le.classes_, le.transform(le.classes_)))
col='b'
df[col+suf]=df[col].map(dic).fillna(dic["c"]).astype(int)
If your data are pd.DataFrame I suggest you this simple solution...
I build a custom transformer that integer maps each categorical feature. When fitted you can transform all the data you want. You can compute also simple label encoding or onehot encoding.
If new unseen categories or NaNs are present in new data:
1] For label encoding, 0 is a special token reserved for mapping these cases.
2] For onehot encoding, all the onehot columns are zeros in these cases.
class FeatureTransformer:
def __init__(self, categorical_features):
self.categorical_features = categorical_features
def fit(self, X):
if not isinstance(X, pd.DataFrame):
raise ValueError("Pass a pandas.DataFrame")
if not isinstance(self.categorical_features, list):
raise ValueError(
"Pass categorical_features as a list of column names")
self.encoding = {}
for c in self.categorical_features:
_, int_id = X[c].factorize()
self.encoding[c] = dict(zip(list(int_id), range(1,len(int_id)+1)))
return self
def transform(self, X, onehot=True):
if not isinstance(X, pd.DataFrame):
raise ValueError("Pass a pandas.DataFrame")
if not hasattr(self, 'encoding'):
raise AttributeError("FeatureTransformer must be fitted")
df = X.drop(self.categorical_features, axis=1)
if onehot: # one-hot encoding
for c in sorted(self.categorical_features):
categories = X[c].map(self.encoding[c]).values
for val in self.encoding[c].values():
df["{}_{}".format(c,val)] = (categories == val).astype('int16')
else: # label encoding
for c in sorted(self.categorical_features):
df[c] = X[c].map(self.encoding[c]).fillna(0)
return df
Usage:
X_train = pd.DataFrame(np.random.randint(10,20, (100,10)))
X_test = pd.DataFrame(np.random.randint(20,30, (100,10)))
ft = FeatureTransformer(categorical_features=[0,1,3])
ft.fit(X_train)
ft.transform(X_test, onehot=False).shape
This is in fact a known bug on LabelEncoder : BUG for fit_transform ... basically you have to fit it and then transform. It will work fine ! A suggestion is to keep a dictionary of your encoders to each and every column so that in the inverse transform you are able to retrieve the original categorical values.
I'm able to mentally process operations better when dealing in DataFrames. The approach below fits and transforms the LabelEncoder() using the training data, then uses a series of pd.merge joins to map the trained fit/transform encoder values to the test data. When there is a test data value not seen in the training data, the code defaults to the max trained encoder value + 1.
# encode class values as integers
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoder.fit(y_train)
encoded_y_train = encoder.transform(y_train)
# make a dataframe of the unique train values and their corresponding encoded integers
y_map = pd.DataFrame({'y_train': y_train, 'encoded_y_train': encoded_y_train})
y_map = y_map.drop_duplicates()
# map the unique test values to the trained encoded integers
y_test_df = pd.DataFrame({'y_test': y_test})
y_test_unique = y_test_df.drop_duplicates()
y_join = pd.merge(y_test_unique, y_map,
left_on = 'y_test', right_on = 'y_train',
how = 'left')
# if the test category is not found in the training category group, then make the
# value the maximum value of the training group + 1
y_join['encoded_y_test'] = np.where(y_join['encoded_y_train'].isnull(),
y_map.shape[0] + 1,
y_join['encoded_y_train']).astype('int')
encoded_y_test = pd.merge(y_test_df, y_join, on = 'y_test', how = 'left') \
.encoded_y_test.values
I found an easy hack around this issue.
Assuming X is the dataframe of features,
First, we need to create a list of dicts which would have the key as the iterable starting from 0 and the corresponding value pair would be the categorical column name. We easily accomplish this using enum.
cat_cols_enum = list(enumerate(X.select_dtypes(include = ['O']).columns))
Then the idea is to create a list of label encoders whose dimension is equal to the number of qualitative(categorical) columns present in the dataframe X.
le = [LabelEncoder() for i in range(len(cat_cols_enum))]
Next and the last part would be fitting each of the label encoders present in the list of encoders with the unique values of each of the categorical columns present in the list of dicts respectively.
for i in cat_cols_enum: le[i[0]].fit(X[i[1]].value_counts().index)
Now, we can transform the labels to their respective encodings using
for i in cat_cols_enum:
X[i[1]] = le[i[0]].transform(X[i[1]])
This error comes when transform function getting any new value for which LabelEncoder try to encode and because in training samples, when you are using fit_transform, that specific value did not present in the corpus. So there is a hack, whether use all the unique values with fit_transform function if you are sure that no new value will come further, or try some different encoding method which suits on the problem statement like HashingEncoder.
Here is the example if no further new values will come in testing
le_id.fit_transform(list(set(df['ID'].unique()).union(set(new_df['ID'].unique()))))
new_df['ID'] = le_id.transform(new_df.ID)
I also encountered the exact same error and was able to fix it.
my array: [[63, 1, 'True' , 1850000000.0, 61666.67]]
When I used the following code, the error occurred
pa_ = preprocessing.LabelEncoder()
pa_.fit([True,False])
data[:,2] = pa_.transform(data[:,2])
The reason for the error was that the true value into array was of string type
And I set boolean. i changed bool type to string Thus, the error was fixed
fit(['True','False'])
I hope my answer be helpful
I hope this helps someone as it's more recent.
sklearn uses the fit_transform to perform the fit function and transform function directing on label encoding.
To solve the problem for Y label throwing error for unseen values, use:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit_transform(Col)
This solves it!
I used
le.fit_transform(Col)
and I was able to resolve the issue. It does fit and transform both. we dont need to worry about unknown values in the test split
Binary one-hot (also known as one-of-K) coding lies in making one binary column for each distinct value for a categorical variable. For example, if one has a color column (categorical variable) that takes the values 'red', 'blue', 'yellow', and 'unknown' then a binary one-hot coding replaces the color column with binaries columns 'color=red', 'color=blue', and 'color=yellow'. I begin with data in a pandas data-frame and I want to use this data to train a model with scikit-learn. I know two ways to do the binary one-hot coding, none of them satisfactory to me.
Pandas and get_dummies in the categorical columns of the data-frame. This method seems excellent as far as the original data-frame contains all data available. That is, you do the one-hot coding before splitting your data in training, validation, and test sets. However, if the data is already split in different sets, this method doesn't work very well. Why? Because one of the data sets (say, the test set) can contain fewer values for a given variable. For example, it can happen that whereas the training set contain the values red, blue, yellow, and unknown for the variable color, the test set only contains red and blue. So the test set would end up having fewer columns than the training set. (I don't know either how the new columns are sorted, and if even having the same columns, this could be in a different order in each set).
Sklearn and DictVectorizer This solves the previous issue, as we can make sure that we are applying the very same transformation to the test set. However, the outcome of the transformation is a numpy array instead of a pandas data-frame. If we want to recover the output as a pandas data-frame, we need to (or at least this is the way I do it): 1) pandas.DataFrame(data=outcome of DictVectorizer transformation, index=index of original pandas data frame, columns= DictVectorizer().get_features_names) and 2) join along the index the resulting data-frame with the original one containing the numerical columns. This works, but it is somewhat cumbersome.
Is there a better way to do a binary one-hot encoding within a pandas data-frame if we have our data split in training and test set?
If your columns are in the same order, you can concatenate the dfs, use get_dummies, and then split them back again, e.g.,
encoded = pd.get_dummies(pd.concat([train,test], axis=0))
train_rows = train.shape[0]
train_encoded = encoded.iloc[:train_rows, :]
test_encoded = encoded.iloc[train_rows:, :]
If your columns are not in the same order, then you'll have challenges regardless of what method you try.
You can set your data type to categorical:
In [5]: df_train = pd.DataFrame({"car":Series(["seat","bmw"]).astype('category',categories=['seat','bmw','mercedes']),"color":["red","green"]})
In [6]: df_train
Out[6]:
car color
0 seat red
1 bmw green
In [7]: pd.get_dummies(df_train )
Out[7]:
car_seat car_bmw car_mercedes color_green color_red
0 1 0 0 0 1
1 0 1 0 1 0
See this issue of Pandas.
If I have a formula as follows:
formula='Price ~ Age + Size + C(Color) + C(Type)'
Where Price,Age, and Size are continuous variables and Color and Type are categorical. If I am loading a dataframe (df) with a column for each variable and selecting the column for 'type' as follows:
types = set(df['type'])
Returned >> ['Cat','Dog','Rabbit','Mouse']
When I go to perform the regression like this:
y,X = dmatrices(formula,df)
model = statsmodels.OLS(y,X)
mdl = model.fit()
mdl.summary()
I go to look for the parameters for each variable and find that there is not a variable included in the summary for some of the categorical values of the dummy variable placed in the model...for example 'T.Cat' is missing...any idea what is going on here? It is especially alarming because the categorical value usually missing (ie. without a tuned parameter) is typically the most common value....