Example: Metadata¶

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This example shows how to add metadata like groups and sample_weight to atom.

Import the wine dataset from sklearn.datasets. This is a small and easy to train dataset whose goal is to predict wines into three groups (which cultivator it's from) using features based on the results of chemical analysis.

Load the data¶

In [1]:

# Import packages
import numpy as np
from sklearn.datasets import load_wine
from atom import ATOMClassifier

# Import packages import numpy as np from sklearn.datasets import load_wine from atom import ATOMClassifier

In [2]:

# Load data
X, y = load_wine(return_X_y=True, as_frame=True)

# Let's have a look
X.head()

# Load data X, y = load_wine(return_X_y=True, as_frame=True) # Let's have a look X.head()

Out[2]:

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0

In [3]:

# Create (dummy) groups and sample_weights for the rows
groups = np.random.randint(5, size=X.shape[0])
sample_weight = np.random.randint(5, size=X.shape[0])
print(groups)

# Create (dummy) groups and sample_weights for the rows groups = np.random.randint(5, size=X.shape[0]) sample_weight = np.random.randint(5, size=X.shape[0]) print(groups)

[3 2 4 4 4 2 3 1 3 4 4 3 4 4 2 0 4 0 4 4 0 0 4 1 4 0 1 4 4 3 1 1 0 0 2 0 3
 0 2 4 1 0 2 4 4 1 1 1 0 3 1 1 0 2 3 4 4 0 1 1 3 3 0 2 0 4 4 4 2 0 0 4 1 0
 2 4 3 0 4 3 1 2 2 0 2 4 2 0 3 0 0 3 4 2 3 1 3 0 2 0 1 2 4 3 3 3 0 2 4 0 4
 3 4 1 4 3 3 0 0 4 2 2 3 2 3 1 0 2 4 0 4 4 0 2 0 2 3 4 4 4 2 4 2 2 4 3 2 2
 4 3 4 2 1 1 0 0 1 4 3 2 3 0 0 4 2 0 1 3 1 4 4 1 1 3 0 1 1 2]

Run the pipeline¶

Add the metadata to the constructor. We leave index=True to prove the group functionality works.
When groups are specified, test_size specifies the number of groups in the test set.

In [4]:

atom = ATOMClassifier(
    X,
    y=y,
    index=True,
    metadata={"groups": groups, "sample_weight": sample_weight},
    test_size=1,
    verbose=2,
    random_state=1,
)

atom = ATOMClassifier( X, y=y, index=True, metadata={"groups": groups, "sample_weight": sample_weight}, test_size=1, verbose=2, random_state=1, )

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

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

Dataset stats ==================== >>
Shape: (178, 14)
Train set size: 145
Test set size: 33
-------------------------------------
Memory: 24.82 kB
Scaled: False
Outlier values: 9 (0.4%)

In [5]:

# Show all rows in the test set belong to the same group
atom.metadata["groups"].loc[atom.test.index]

# Show all rows in the test set belong to the same group atom.metadata["groups"].loc[atom.test.index]

Out[5]:

34     2
120    2
53     2
68     2
151    2
81     2
164    2
5      2
135    2
140    2
143    2
74     2
146    2
133    2
38     2
142    2
63     2
127    2
123    2
86     2
84     2
159    2
1      2
101    2
177    2
42     2
121    2
107    2
147    2
93     2
98     2
82     2
14     2
Name: groups, dtype: int64

In [14]:

# Visualize the groups
atom.plot_data_splits()

# Visualize the groups atom.plot_data_splits()

In [6]:

atom.scale()

atom.scale()

Fitting Scaler...
Scaling features...

In [7]:

# Note the sample weights are passed to the scaler
atom.pipeline[0].get_metadata_routing()

# Note the sample weights are passed to the scaler atom.pipeline[0].get_metadata_routing()

Out[7]:

{'fit': {'sample_weight': True}}

In [9]:

atom.run("LR")

atom.run("LR")

Training ========================= >>
Models: LR
Metric: f1_weighted


Results for LogisticRegression:
Fit ---------------------------------------------
Train evaluation --> f1_weighted: 1.0
Test evaluation --> f1_weighted: 1.0
Time elapsed: 0.036s
-------------------------------------------------
Time: 0.036s


Final results ==================== >>
Total time: 0.040s
-------------------------------------
LogisticRegression --> f1_weighted: 1.0

In [10]:

# The same applies to models...
atom.lr.estimator.get_metadata_routing()

# The same applies to models... atom.lr.estimator.get_metadata_routing()

Out[10]:

{'fit': {'sample_weight': True}, 'score': {'sample_weight': None}}

In [11]:

# ... and metrics
atom._metric[0].get_metadata_routing()

# ... and metrics atom._metric[0].get_metadata_routing()

Out[11]:

{'score': {'sample_weight': True}}

In [16]:

atom.lr.cross_validate()

atom.lr.cross_validate()

Applying cross-validation...

Out[16]:

	train_f1_weighted	test_f1_weighted	time
0	1.000000	0.985135	0.024023
1	1.000000	1.000000	0.022020
2	1.000000	0.962963	0.017015
3	1.000000	1.000000	0.015013
4	1.000000	1.000000	0.020018
mean	1.000000	0.989620	0.019618

In [17]:

atom.plot_cv_splits()

atom.plot_cv_splits()