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import sys
import pickle
import csv
import numpy as np
import Algorithmia
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
client = Algorithmia.client()
def load_model():
# Get file by name
# Open file and load model
file_path = 'data://YOUR_USERNAME/scikit_learn_demo/scikit-demo-boston-regression.pkl'
model_path = client.file(file_path).getFile().name
# Open file and load model
with open(model_path, 'rb') as f:
model = pickle.load(f)
return model
# Load model outside of the apply function so it only gets loaded once
model = load_model()
def process_input(input):
# Create numpy array from csv file passed as input in apply()
if input.startswith('data:'):
file_url = client.file(input).getFile().name
np_array = np.genfromtxt(file_url, delimiter=',')
return np_array
except Exception as e:
print("Could not create numpy array from data", e)
def apply(input):
# Input should be a csv file - model is trained on Sklearn
# Boston housing dataset using RandomForestRegressor
np_data = process_input(input)
prediction = model.predict(np_data)
return list(prediction)
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