How to pass a single feature of a data set to train using sklearn KNeighborsClassifier and predict value?

Question

SO I read a csv dataset and then store it using pandas dataframe, I then split the data into training and testing set. What I am trying to accomplish is to train and predict accuracy using only one feature at a time so that I can later see what feature would be best predictor out of the 4. I'm new to python and machine learning, so please bare w me. This is actually the first time I actually try both. I get an error in this line my_knn_for_cs4661.fit(X_train[col], y_train) something about array.reshape(-1,1) I have tried to do X_train[col].reshape(-1,1) but I get some other errors. I am using python 3 on jupyter notebook, sklearn, numpy, and pandas.

Below is my code and error

from sklearn.model_selection import train_test_split

iris_df = pd.read_csv('https://raw.githubusercontent.com/mpourhoma/CS4661/master/iris.csv')
feature_cols = ['sepal_length','sepal_width','petal_length','petal_width']
X = iris_df[feature_cols] 
y = iris_df['species']
predictions= {}

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=6)

k = 3
my_knn_for_cs4661 = KNeighborsClassifier(n_neighbors=k)

for col in feature_cols:

    my_knn_for_cs4661.fit(X_train[col], y_train)
    y_predict = my_knn_for_cs4661.predict(X_test)
    predictions[col] = y_predict

My Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-41-933eb8b496d8> in <module>()
     13 for col in feature_cols:
     14 
---> 15     my_knn_for_cs4661.fit(X_train[col], y_train)
     16     y_predict = my_knn_for_cs4661.predict(X_test)
     17     predictions[col] = y_predict

~\Anaconda3\lib\site-packages\sklearn\neighbors\base.py in fit(self, X, y)
    763         """
    764         if not isinstance(X, (KDTree, BallTree)):
--> 765             X, y = check_X_y(X, y, "csr", multi_output=True)
    766 
    767         if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    571     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,
    572                     ensure_2d, allow_nd, ensure_min_samples,
--> 573                     ensure_min_features, warn_on_dtype, estimator)
    574     if multi_output:
    575         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    439                     "Reshape your data either using array.reshape(-1, 1) if "
    440                     "your data has a single feature or array.reshape(1, -1) "
--> 441                     "if it contains a single sample.".format(array))
    442             array = np.atleast_2d(array)
    443             # To ensure that array flags are maintained

ValueError: Expected 2D array, got 1D array instead:
array=[6.  5.  5.7 6.3 5.6 5.6 4.6 5.8 5.8 4.7 5.5 5.4 5.8 6.4 6.5 6.7 6.1 6.9
 7.2 6.2 5.1 4.9 6.5 6.8 5.1 4.6 5.7 7.9 6.1 6.3 6.8 5.5 6.3 6.7 5.5 5.
 7.3 4.4 5.3 4.8 4.5 4.6 5.  5.8 6.9 4.8 7.7 5.8 5.4 6.7 5.5 6.7 5.9 5.6
 5.  6.  5.9 7.  5.4 4.9 5.  5.2 6.  5.1 6.1 6.2 5.6 6.7 6.8 5.8 6.7 5.7
 7.2 5.4 7.4 4.4 6.2 6.5 5.  6.7 6.6 4.9 5.  6.  5.5 6.2 5.7 7.2 4.9 6. ].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Answer 1

We can use

array.values.reshape(-1,1)

values covert the series into 1-D array on which reshape is used to convert it into 2-D array

Answer 2

I found a solution although it seems hacky, IDK if this is the pythonic way.

iris_df = pd.read_csv('https://raw.githubusercontent.com/mpourhoma/CS4661/master/iris.csv')
feature_cols = ['sepal_length','sepal_width','petal_length','petal_width']
X = iris_df[feature_cols] 
y = iris_df['species']
predictions= {}

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=6)

k = 3
my_knn_for_cs4661 = KNeighborsClassifier(n_neighbors=k)

for col in feature_cols:
    my_knn_for_cs4661.fit(X_train[col].values.reshape(-1,1), y_train)
    y_predict = my_knn_for_cs4661.predict(X_test[col].values.reshape(-1,1))
    predictions[col] = accuracy_score(y_test, y_predict)


print(predictions)

Answer 3

Expected 2D array, got 1D array instead means when you implement KNeighborClassifier, your training dataset must contain at least two features, for example

X_train[['sepal_length', 'sepal_width']]