IntermediateML pipelines
This notebook is an exercise in the Intermediate Machine Learning course. You can reference the tutorial at this link.
In this exercise, you will use pipelines to improve the efficiency of your machine learning code.
Setup
The questions below will give you feedback on your work. Run the following cell to set up the feedback system.
# Set up code checking
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv")
from learntools.core import binder
binder.bind(globals())
from learntools.ml_intermediate.ex4 import *
print("Setup Complete")
Setup Complete
You will work with data from the Housing Prices Competition for Kaggle Learn Users.
Run the next code cell without changes to load the training and validation sets in X_train, X_valid, y_train, and y_valid. The test set is loaded in X_test.
import pandas as pd
from sklearn.model_selection import train_test_split
# Read the data
X_full = pd.read_csv('../input/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/test.csv', index_col='Id')
# Remove rows with missing target, separate target from predictors
X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X_full.SalePrice
X_full.drop(['SalePrice'], axis=1, inplace=True)
# Break off validation set from training data
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y,
train_size=0.8, test_size=0.2,
random_state=0)
# "Cardinality" means the number of unique values in a column
# Select categorical columns with relatively low cardinality (convenient but arbitrary)
categorical_cols = [cname for cname in X_train_full.columns if
X_train_full[cname].nunique() < 10 and
X_train_full[cname].dtype == "object"]
# Select numerical columns
numerical_cols = [cname for cname in X_train_full.columns if
X_train_full[cname].dtype in ['int64', 'float64']]
# Keep selected columns only
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()
X_train.head()
| MSZoning | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Condition1 | Condition2 | ... | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | |||||||||||||||||||||
| 619 | RL | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | Norm | Norm | ... | 774 | 0 | 108 | 0 | 0 | 260 | 0 | 0 | 7 | 2007 |
| 871 | RL | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | PosN | Norm | ... | 308 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 2009 |
| 93 | RL | Pave | Grvl | IR1 | HLS | AllPub | Inside | Gtl | Norm | Norm | ... | 432 | 0 | 0 | 44 | 0 | 0 | 0 | 0 | 8 | 2009 |
| 818 | RL | Pave | NaN | IR1 | Lvl | AllPub | CulDSac | Gtl | Norm | Norm | ... | 857 | 150 | 59 | 0 | 0 | 0 | 0 | 0 | 7 | 2008 |
| 303 | RL | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Norm | Norm | ... | 843 | 468 | 81 | 0 | 0 | 0 | 0 | 0 | 1 | 2006 |
5 rows × 76 columns
The next code cell uses code from the tutorial to preprocess the data and train a model. Run this code without changes.
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
# Preprocessing for numerical data
numerical_transformer = SimpleImputer(strategy='constant')
# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='most_frequent')),
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, numerical_cols),
('cat', categorical_transformer, categorical_cols)
])
# Define model
model = RandomForestRegressor(n_estimators=100, random_state=0)
# Bundle preprocessing and modeling code in a pipeline
clf = Pipeline(steps=[('preprocessor', preprocessor),
('model', model)
])
# Preprocessing of training data, fit model
clf.fit(X_train, y_train)
# Preprocessing of validation data, get predictions
preds = clf.predict(X_valid)
print('MAE:', mean_absolute_error(y_valid, preds))
MAE: 17861.780102739725
The code yields a value around 17862 for the mean absolute error (MAE). In the next step, you will amend the code to do better.
Step 1: Improve the performance
Part A
Now, it’s your turn! In the code cell below, define your own preprocessing steps and random forest model. Fill in values for the following variables:
numerical_transformercategorical_transformermodel
To pass this part of the exercise, you need only define valid preprocessing steps and a random forest model.
X_train.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1168 entries, 619 to 685
Data columns (total 76 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 MSZoning 1168 non-null object
1 Street 1168 non-null object
2 Alley 71 non-null object
3 LotShape 1168 non-null object
4 LandContour 1168 non-null object
5 Utilities 1168 non-null object
6 LotConfig 1168 non-null object
7 LandSlope 1168 non-null object
8 Condition1 1168 non-null object
9 Condition2 1168 non-null object
10 BldgType 1168 non-null object
11 HouseStyle 1168 non-null object
12 RoofStyle 1168 non-null object
13 RoofMatl 1168 non-null object
14 MasVnrType 1162 non-null object
15 ExterQual 1168 non-null object
16 ExterCond 1168 non-null object
17 Foundation 1168 non-null object
18 BsmtQual 1140 non-null object
19 BsmtCond 1140 non-null object
20 BsmtExposure 1140 non-null object
21 BsmtFinType1 1140 non-null object
22 BsmtFinType2 1139 non-null object
23 Heating 1168 non-null object
24 HeatingQC 1168 non-null object
25 CentralAir 1168 non-null object
26 Electrical 1167 non-null object
27 KitchenQual 1168 non-null object
28 Functional 1168 non-null object
29 FireplaceQu 617 non-null object
30 GarageType 1110 non-null object
31 GarageFinish 1110 non-null object
32 GarageQual 1110 non-null object
33 GarageCond 1110 non-null object
34 PavedDrive 1168 non-null object
35 PoolQC 4 non-null object
36 Fence 214 non-null object
37 MiscFeature 49 non-null object
38 SaleType 1168 non-null object
39 SaleCondition 1168 non-null object
40 MSSubClass 1168 non-null int64
41 LotFrontage 956 non-null float64
42 LotArea 1168 non-null int64
43 OverallQual 1168 non-null int64
44 OverallCond 1168 non-null int64
45 YearBuilt 1168 non-null int64
46 YearRemodAdd 1168 non-null int64
47 MasVnrArea 1162 non-null float64
48 BsmtFinSF1 1168 non-null int64
49 BsmtFinSF2 1168 non-null int64
50 BsmtUnfSF 1168 non-null int64
51 TotalBsmtSF 1168 non-null int64
52 1stFlrSF 1168 non-null int64
53 2ndFlrSF 1168 non-null int64
54 LowQualFinSF 1168 non-null int64
55 GrLivArea 1168 non-null int64
56 BsmtFullBath 1168 non-null int64
57 BsmtHalfBath 1168 non-null int64
58 FullBath 1168 non-null int64
59 HalfBath 1168 non-null int64
60 BedroomAbvGr 1168 non-null int64
61 KitchenAbvGr 1168 non-null int64
62 TotRmsAbvGrd 1168 non-null int64
63 Fireplaces 1168 non-null int64
64 GarageYrBlt 1110 non-null float64
65 GarageCars 1168 non-null int64
66 GarageArea 1168 non-null int64
67 WoodDeckSF 1168 non-null int64
68 OpenPorchSF 1168 non-null int64
69 EnclosedPorch 1168 non-null int64
70 3SsnPorch 1168 non-null int64
71 ScreenPorch 1168 non-null int64
72 PoolArea 1168 non-null int64
73 MiscVal 1168 non-null int64
74 MoSold 1168 non-null int64
75 YrSold 1168 non-null int64
dtypes: float64(3), int64(33), object(40)
memory usage: 702.6+ KB
# Preprocessing for numerical data
numerical_transformer = SimpleImputer(strategy='mean')
# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant')),
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, numerical_cols),
('cat', categorical_transformer, categorical_cols)
])
# Define model
model = RandomForestRegressor(n_estimators=100, random_state=0)
# Check your answer
step_1.a.check()
<IPython.core.display.Javascript object>
Correct
# Lines below will give you a hint or solution code
step_1.a.hint()
#step_1.a.solution()
<IPython.core.display.Javascript object>
Hint: While there are many different potential solutions to this problem, we achieved satisfactory results by changing only column_transformer from the default value - specifically, we changed the strategy parameter that decides how missing values are imputed.
Part B
Run the code cell below without changes.
To pass this step, you need to have defined a pipeline in Part A that achieves lower MAE than the code above. You’re encouraged to take your time here and try out many different approaches, to see how low you can get the MAE! (If your code does not pass, please amend the preprocessing steps and model in Part A.)
# Bundle preprocessing and modeling code in a pipeline
my_pipeline = Pipeline(steps=[('preprocessor', preprocessor),
('model', model)
])
# Preprocessing of training data, fit model
my_pipeline.fit(X_train, y_train)
# Preprocessing of validation data, get predictions
preds = my_pipeline.predict(X_valid)
# Evaluate the model
score = mean_absolute_error(y_valid, preds)
print('MAE:', score)
# Check your answer
step_1.b.check()
MAE: 17479.87044520548
<IPython.core.display.Javascript object>
Correct
# Line below will give you a hint
#step_1.b.hint()
Step 2: Generate test predictions
Now, you’ll use your trained model to generate predictions with the test data.
# Preprocessing of test data, fit model
preds_test = my_pipeline.predict(X_test)
# Check your answer
step_2.check()
<IPython.core.display.Javascript object>
Correct
# Lines below will give you a hint or solution code
#step_2.hint()
#step_2.solution()
Run the next code cell without changes to save your results to a CSV file that can be submitted directly to the competition.
# Save test predictions to file
output = pd.DataFrame({'Id': X_test.index,
'SalePrice': preds_test})
output.to_csv('submission.csv', index=False)
output.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1459 entries, 0 to 1458
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Id 1459 non-null int64
1 SalePrice 1459 non-null float64
dtypes: float64(1), int64(1)
memory usage: 22.9 KB
Submit your results
Once you have successfully completed Step 2, you’re ready to submit your results to the leaderboard! If you choose to do so, make sure that you have already joined the competition by clicking on the Join Competition button at this link.
- Begin by clicking on the Save Version button in the top right corner of the window. This will generate a pop-up window.
- Ensure that the Save and Run All option is selected, and then click on the Save button.
- This generates a window in the bottom left corner of the notebook. After it has finished running, click on the number to the right of the Save Version button. This pulls up a list of versions on the right of the screen. Click on the ellipsis (…) to the right of the most recent version, and select Open in Viewer. This brings you into view mode of the same page. You will need to scroll down to get back to these instructions.
- Click on the Output tab on the right of the screen. Then, click on the file you would like to submit, and click on the Submit button to submit your results to the leaderboard.
You have now successfully submitted to the competition!
If you want to keep working to improve your performance, select the Edit button in the top right of the screen. Then you can change your code and repeat the process. There’s a lot of room to improve, and you will climb up the leaderboard as you work.
Keep going
Move on to learn about cross-validation, a technique you can use to obtain more accurate estimates of model performance!
Have questions or comments? Visit the course discussion forum to chat with other learners.
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