From 44ed3862e8de325a20a9b192e2d36488448ac587 Mon Sep 17 00:00:00 2001 From: Jongsung-Kim Date: Wed, 27 Sep 2017 01:33:23 +0900 Subject: [PATCH 1/2] Update income_classifier.py The code is not running on my environment. becauese the transform() argument type is will be list. So, I try to change the input_data[i] -> [input_data[i]] then, It works. --- Chapter 02/code/income_classifier.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Chapter 02/code/income_classifier.py b/Chapter 02/code/income_classifier.py index 740095f..42518d7 100644 --- a/Chapter 02/code/income_classifier.py +++ b/Chapter 02/code/income_classifier.py @@ -75,7 +75,8 @@ if item.isdigit(): input_data_encoded[i] = int(input_data[i]) else: - input_data_encoded[i] = int(label_encoder[count].transform(input_data[i])) + #input_data_encoded[i] = int(label_encoder[count].transform(input_data[i])) + input_data_encoded[i] = int(label_encoder[count].transform([input_data[i]])) count += 1 input_data_encoded = np.array(input_data_encoded) From 57830dea8ac91f018413d77f79453157165a8369 Mon Sep 17 00:00:00 2001 From: Jongsung-Kim Date: Thu, 28 Sep 2017 23:48:23 +0900 Subject: [PATCH 2/2] Update income_classifier.py classifier.predict() function's argument expected 2D array. So, I change the classifier.predict(input_data_encoded) to classifier.predict([input_data_encoded]) then... It works. --- Chapter 02/code/income_classifier.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Chapter 02/code/income_classifier.py b/Chapter 02/code/income_classifier.py index 42518d7..ed13549 100644 --- a/Chapter 02/code/income_classifier.py +++ b/Chapter 02/code/income_classifier.py @@ -75,13 +75,12 @@ if item.isdigit(): input_data_encoded[i] = int(input_data[i]) else: - #input_data_encoded[i] = int(label_encoder[count].transform(input_data[i])) input_data_encoded[i] = int(label_encoder[count].transform([input_data[i]])) count += 1 input_data_encoded = np.array(input_data_encoded) # Run classifier on encoded datapoint and print output -predicted_class = classifier.predict(input_data_encoded) +predicted_class = classifier.predict([input_data_encoded]) print(label_encoder[-1].inverse_transform(predicted_class)[0])