ValueError: Cannot Convert String to Float With Pandas and Amazon Sagemaker - pandas

Question

I'm trying to deploy a simple ML model on SageMaker to get the hang of it, and I am not having any luck because I get the following error:
ValueError: could not convert string to float: '6.320000000000000097e-03 1.800000000000000000e+01 2.310000000000000053e+00 0.000000000000000000e+00 5.380000000000000338e-01 6.575000000000000178e+00 6.520000000000000284e+01 4.089999999999999858e+00 1.000000000000000000e+00 2.960000000000000000e+02 1.530000000000000071e+01 3.968999999999999773e+02 4.980000000000000426e+00 2.400000000000000000e+01'
This is the first row of my dataframe.
This is the code in my notebook that I'm using right now:
from sagemaker import get_execution_role, Session
from sagemaker.sklearn.estimator import SKLearn
work_dir = 'data'
session = Session()
role = get_execution_role()
train_input = session.upload_data('data')
script = 'boston_housing_prep.py'
model = SKLearn(
entry_point = script,
train_instance_type = 'ml.c4.xlarge',
role = role,
sagemaker_session = session,
hyperparameters = {'alpha': 10}
)
model.fit({'train': train_input})
My script for boston_housing_prep.py looks like this:
import argparse
import pandas as pd
import os
from sklearn.linear_model import Ridge
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler
import numpy as np
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--alpha', type=int, default=1)
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
args = parser.parse_args()
input_files = [ os.path.join(args.train, file) for file in os.listdir(args.train) ]
if len(input_files) == 0:
raise ValueError(('There are no files in {}.\n' +
'This usually indicates that the channel ({}) was incorrectly specified,\n' +
'the data specification in S3 was incorrectly specified or the role specified\n' +
'does not have permission to access the data.').format(args.train, "train"))
raw_data = [ pd.read_csv(file, header=None, engine="python") for file in input_files ]
df = pd.concat(raw_data)
y_train = df.iloc[:, -1]
X_train = df.iloc[:, :5]
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
alpha = args.alpha
clf = Ridge(alpha=alpha)
clf = clf.fit(X_train, y_train)
joblib.dump(clf, os.path.join(args.model_dir, "model.joblib"))
def model_fn(model_dir):
clf = joblib.load(os.path.join(model_dir, "model.joblib"))
return clf
The line that's giving the problem is this one:
X_train = scaler.fit_transform(X_train)
I tried df = df.astype(np.float) after I loaded in the df, but that didn't work either.
This file loads in without a problem when I'm not in SageMaker.