Error using sklearn and linear regression shapes 1 16 and 1 1 not aligned 16 dim 1 1 dim 0

Question

I wanted to learn about machine learning and I stumbled upon youtube siraj and his Udacity videos and wanted to try and pick up a few things.

In his video, he had a txt file he imported and read, but when I tried to recreate the the txt file it couldn't be read correctly. Instead, I tried to create a pandas dataframe with the same data and perform the linear regression/predict on it, but then I got the below error.

Found input variables with inconsistent numbers of samples: [1, 16] and something about passing 1d arrays and I need to reshape them.

I get this error....

shapes (1,16) and (1,1) not aligned: 16 (dim 1) != 1 (dim 0)

This is my code down below. I know it's probably a syntax error, I'm just not familiar with this scklearn yet and would like some help.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model

#DF = pd.read_fwf('BrainBodyWeight.txt')
DF = pd.DataFrame()
DF['Brain'] = [3.385, .480, 1.350, 465.00,36.330, 27.660, 14.830, 1.040, 4.190, 0.425, 0.101, 0.920, 1.000, 0.005, 0.060, 3.500 ]

DF['Body'] = [44.500, 15.5, 8.1, 423, 119.5, 115, 98.2, 5.5,58, 6.40, 4, 5.7,6.6, .140,1, 10.8]

try:
    x = DF['Brain']
    y = DF['Body']

    x = x.tolist()
    y = y.tolist()

    x = np.asarray(x)
    y = np.asarray(y)


    body_reg = linear_model.LinearRegression()
    body_reg.fit(x.reshape(-1,1),y.reshape(-1,1))
    plt.scatter(x,y)
    plt.plot(x,body_reg.predict(x))
    plt.show()
except Exception as e:
    print(e)

Can anyone explain why sklearn doesn't like my input????

Answer 1

Based on documents LinearRegression. An x array of the shape [n samples,n features] is required by fit(). That's why, before executing fit, you're altering your x array. There are no n features given because if you don't, you'll end up with an array with the shape (16,), which does not fit the needed [n samples,n features] shape.

z = DF['Brain']
z = z.tolist()
z = np.asarray(z)

# 16 samples, None feature
z.shape
(16,)

# 16 samples, 1 feature
z.reshape(-1,1).shape
(16,1)

The LinearRegression has the same criteria. When calling the predict function (and also for consistency), you only need to conduct the same reshaping as before.

plt.plot(z,body_reg.predict(z.reshape(-1,1)))

You can also simply restructure the x array before executing any functions. 
You can also access the inner numpy array of values for feature reference by calling DF['Brain'].values. You don't need to cast it to numpy array -> list. So instead of doing all the conversions, you can just use this:

z = DF['Brain'].values.reshape(1,-1)
y = DF['Body'].values.reshape(1,-1)

reg = linear_model.LinearRegression()
reg.fit(z, y)

Hope this helps!

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