Example: Automated feature scaling¶

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This example shows how ATOM handles models that require automated feature scaling.

Import the breast cancer dataset from sklearn.datasets. This is a small and easy to train dataset whose goal is to predict whether a patient has breast cancer or not.

Load the data¶

In [1]:

# Import packages
from sklearn.datasets import load_breast_cancer
from atom import ATOMClassifier

# Import packages from sklearn.datasets import load_breast_cancer from atom import ATOMClassifier

In [2]:

# Load the data
X, y = load_breast_cancer(return_X_y=True)

# Load the data X, y = load_breast_cancer(return_X_y=True)

Run the pipeline¶

In [3]:

atom = ATOMClassifier(X, y, verbose=2, random_state=1)

atom = ATOMClassifier(X, y, verbose=2, random_state=1)

<< ================== ATOM ================== >>

Configuration ==================== >>
Algorithm task: Binary classification.

Dataset stats ==================== >>
Shape: (569, 31)
Train set size: 456
Test set size: 113
-------------------------------------
Memory: 141.24 kB
Scaled: False
Outlier values: 167 (1.2%)

In [4]:

# Check which models require feature scaling
atom.available_models(needs_scaling=True)

# Check which models require feature scaling atom.available_models(needs_scaling=True)

Out[4]:

	acronym	fullname	estimator	module	handles_missing	needs_scaling	accepts_sparse	native_multilabel	native_multioutput	validation	supports_engines
0	CatB	CatBoost	CatBoostClassifier	catboost.core	True	True	True	False	False	n_estimators	catboost
1	KNN	KNearestNeighbors	KNeighborsClassifier	sklearn.neighbors._classification	False	True	True	True	True	None	sklearn, sklearnex, cuml
2	LGB	LightGBM	LGBMClassifier	lightgbm.sklearn	True	True	True	False	False	n_estimators	lightgbm
3	lSVM	LinearSVM	LinearSVC	sklearn.svm._classes	False	True	True	False	False	None	sklearn, cuml
4	LR	LogisticRegression	LogisticRegression	sklearn.linear_model._logistic	False	True	True	False	False	None	sklearn, sklearnex, cuml
5	MLP	MultiLayerPerceptron	MLPClassifier	sklearn.neural_network._multilayer_perceptron	False	True	True	True	False	max_iter	sklearn
6	PA	PassiveAggressive	PassiveAggressiveClassifier	sklearn.linear_model._passive_aggressive	False	True	True	False	False	max_iter	sklearn
7	Perc	Perceptron	Perceptron	sklearn.linear_model._perceptron	False	True	False	False	False	max_iter	sklearn
8	RNN	RadiusNearestNeighbors	RadiusNeighborsClassifier	sklearn.neighbors._classification	False	True	True	True	True	None	sklearn
9	Ridge	Ridge	RidgeClassifier	sklearn.linear_model._ridge	False	True	True	True	False	None	sklearn, sklearnex, cuml
10	SGD	StochasticGradientDescent	SGDClassifier	sklearn.linear_model._stochastic_gradient	False	True	True	False	False	max_iter	sklearn
11	SVM	SupportVectorMachine	SVC	sklearn.svm._classes	False	True	True	False	False	None	sklearn, sklearnex, cuml
12	XGB	XGBoost	XGBClassifier	xgboost.sklearn	True	True	True	False	False	n_estimators	xgboost

In [5]:

# We fit two models: LR needs scaling and Bag doesn't
atom.run(["LR", "Bag"])

# We fit two models: LR needs scaling and Bag doesn't atom.run(["LR", "Bag"])

Training ========================= >>
Models: LR, Bag
Metric: f1


Results for LogisticRegression:
Fit ---------------------------------------------
Train evaluation --> f1: 0.9913
Test evaluation --> f1: 0.9861
Time elapsed: 0.120s
-------------------------------------------------
Time: 0.120s


Results for Bagging:
Fit ---------------------------------------------
Train evaluation --> f1: 0.9982
Test evaluation --> f1: 0.9444
Time elapsed: 0.067s
-------------------------------------------------
Time: 0.067s


Final results ==================== >>
Total time: 0.194s
-------------------------------------
LogisticRegression --> f1: 0.9861 !
Bagging            --> f1: 0.9444

In [6]:

# Now, we create a new branch and scale the features before fitting the model
atom.branch = "scaling"

# Now, we create a new branch and scale the features before fitting the model atom.branch = "scaling"

Successfully created new branch: scaling.

In [7]:

atom.scale()

atom.scale()

Fitting Scaler...
Scaling features...

In [8]:

atom.run("LR_2")

atom.run("LR_2")

Training ========================= >>
Models: LR_2
Metric: f1


Results for LogisticRegression:
Fit ---------------------------------------------
Train evaluation --> f1: 0.9913
Test evaluation --> f1: 0.9861
Time elapsed: 0.032s
-------------------------------------------------
Time: 0.032s


Final results ==================== >>
Total time: 0.035s
-------------------------------------
LogisticRegression --> f1: 0.9861

Analyze the results¶

In [9]:

# Let's compare the differences between the models
print(atom.lr.scaler)
print(atom.bag.scaler)
print(atom.lr_2.scaler)

# Let's compare the differences between the models print(atom.lr.scaler) print(atom.bag.scaler) print(atom.lr_2.scaler)

Scaler()
None
None

In [10]:

# And the data they use is different
print(atom.lr.X.iloc[:5, :3])
print("-----------------------------")
print(atom.bag.X.iloc[:5, :3])
print("-----------------------------")
print(atom.lr_2.X_train.equals(atom.lr.X_train))

# And the data they use is different print(atom.lr.X.iloc[:5, :3]) print("-----------------------------") print(atom.bag.X.iloc[:5, :3]) print("-----------------------------") print(atom.lr_2.X_train.equals(atom.lr.X_train))

         x0        x1        x2
0 -0.181875  0.356669 -0.147122
1  1.162216  0.300578  1.159704
2  1.056470  1.212060  0.933833
3  0.277287  2.457753  0.188054
4 -1.442482 -0.825921 -1.343434
-----------------------------
      x0     x1      x2
0  13.48  20.82   88.40
1  18.31  20.58  120.80
2  17.93  24.48  115.20
3  15.13  29.81   96.71
4   8.95  15.76   58.74
-----------------------------
True

In [11]:

# Note that the scaler is included in the model's pipeline
print(atom.lr.pipeline)
print("-----------------------------")
print(atom.bag.pipeline)
print("-----------------------------")
print(atom.lr_2.pipeline)

# Note that the scaler is included in the model's pipeline print(atom.lr.pipeline) print("-----------------------------") print(atom.bag.pipeline) print("-----------------------------") print(atom.lr_2.pipeline)

Pipeline(memory=Memory(location=None),
         ('steps', [('AutomatedScaler', Scaler())]),
         verbose=False)
-----------------------------
Pipeline(memory=Memory(location=None), steps=[], verbose=False)
-----------------------------
Pipeline(memory=Memory(location=None),
         ('steps', [('scaler', Scaler(verbose=2))]),
         verbose=False)

In [12]:

atom.plot_pipeline()

atom.plot_pipeline()