CODEWITHSUNDEEP
pythonpandasscikit-learn
Luleo_Primoc
Luleo_Primoc

Reputation: 105

Getting error saying column not present in scikit learn/pandas function

I am trying to train this random classifier to see if my preprocessing works. I think I made a mistake separating my training data and labels as I see in the error message (Price). But I do not know exactly what is wrong.

Code:

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import RandomForestClassifier


def diamond_preprocess(data_dir):
    data = pd.read_csv(data_dir)
    cleaned_data = data.drop(['id', 'depth_percent'], axis=1)  # Features I don't want

    x = cleaned_data.drop(['price'], axis=1)  # Train data
    y = cleaned_data['price']  # Label data

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

    numerical_features = cleaned_data.select_dtypes(include=['int64', 'float64']).columns
    categorical_features = cleaned_data.select_dtypes(include=['object']).columns

    numerical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='median')),  # Fill in missing data with median
        ('scaler', StandardScaler())  # Scale data
    ])

    categorical_transformer = Pipeline(steps=[
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),  # Fill in missing data with 'missing'
        ('onehot', OneHotEncoder(handle_unknown='ignore'))  # One hot encode categorical data
    ])

    preprocessor_pipeline = ColumnTransformer(
        transformers=[
            ('num', numerical_transformer, numerical_features),
            ('cat', categorical_transformer, categorical_features)
        ])

    rf = Pipeline(steps=[('preprocessor', preprocessor_pipeline),
                         ('classifier', RandomForestClassifier())])

    rf.fit(x_train, y_train)

cleaned_data.columns: Index(['carat', 'cut', 'color', 'clarity', 'table', 'price', 'length', 'width', 'depth'], dtype='object')

Error:

  File "pandas\_libs\hashtable_class_helper.pxi", line 4562, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'price'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\Users\17574\Anaconda3\envs\kraken-gpu\lib\site-packages\sklearn\utils\__init__.py", line 396, in _get_column_indices
    col_idx = all_columns.get_loc(col)
  File "C:\Users\17574\Anaconda3\envs\kraken-gpu\lib\site-packages\pandas\core\indexes\base.py", line 3082, in get_loc
    raise KeyError(key) from err
KeyError: 'price'

The above exception was the direct cause of the following exception:

ValueError: A given column is not a column of the dataframe

It seems to be mad that I am feeding x_train (which has price excluded as it is my training data) into the preprocessing pipeline which includes the 'price' feature. This shouldn't be a problem because my labels are all 'price' integers and need to be preprocessed right? Do I need a separate transformer just for labels?

Upvotes: 0

Views: 828

Answers (1)

Antoine Dubuis
Antoine Dubuis

Reputation: 5304

You are performing your ColumnTransformer based on columns defined in the cleaned_data DataFrame instead of columns defined in x_train.

You can either modify your categorical and numerical features by computing them from x_train as follows:

 numerical_features = x_train.select_dtypes(include=['int64', 'float64']).columns
    categorical_features = x_train.select_dtypes(include=['object']).columns

Or even better, by using sklearn.compose.make_column_selector to perform the selection as follows:

from sklearn.compose import make_column_selector
preprocessor_pipeline = ColumnTransformer(
        transformers=[
            ('num', numerical_transformer, make_column_selector(dtype_exclude=object)),
            ('cat', categorical_transformer, make_column_selector(dtype_include=object))
        ])

Upvotes: 1

Related Questions