Regression¶

---

This example shows how to use ATOM to apply pca on the data and run a regression pipeline.

Download the abalone dataset from https://archive.ics.uci.edu/ml/datasets/Abalone. The goal of this dataset is to predict the rings (age) of abalone shells from physical measurements.

Load the data¶

In [1]:

# Import packages
import pandas as pd
from atom import ATOMRegressor

# Import packages import pandas as pd from atom import ATOMRegressor

In [2]:

# Load the data
X = pd.read_csv("./datasets/abalone.csv")

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

# Load the data X = pd.read_csv("./datasets/abalone.csv") # Let's have a look X.head()

Out[2]:

	Sex	Length	Diameter	Height	Whole weight	Shucked weight	Viscera weight	Shell weight	Rings
0	M	0.455	0.365	0.095	0.5140	0.2245	0.1010	0.150	15
1	M	0.350	0.265	0.090	0.2255	0.0995	0.0485	0.070	7
2	F	0.530	0.420	0.135	0.6770	0.2565	0.1415	0.210	9
3	M	0.440	0.365	0.125	0.5160	0.2155	0.1140	0.155	10
4	I	0.330	0.255	0.080	0.2050	0.0895	0.0395	0.055	7

In [3]:

# Initialize atom for regression tasks
atom = ATOMRegressor(X, "Rings", verbose=2, random_state=42)

# Initialize atom for regression tasks atom = ATOMRegressor(X, "Rings", verbose=2, random_state=42)

<< ================== ATOM ================== >>
Algorithm task: regression.

Dataset stats ==================== >>
Shape: (4177, 9)
Memory: 509.72 kB
Scaled: False
Categorical features: 1 (12.5%)
Outlier values: 196 (0.7%)
-------------------------------------
Train set size: 3342
Test set size: 835

In [4]:

# Encode the categorical features
atom.encode()

# Encode the categorical features atom.encode()

Fitting Encoder...
Encoding categorical columns...
 --> OneHot-encoding feature Sex. Contains 3 classes.

In [5]:

# Plot the dataset's correlation matrix
atom.plot_correlation()

# Plot the dataset's correlation matrix atom.plot_correlation()

In [6]:

# Apply pca for dimensionality reduction
atom.feature_selection(strategy="pca", n_features=6)

# Apply pca for dimensionality reduction atom.feature_selection(strategy="pca", n_features=6)

Fitting FeatureSelector...
Performing feature selection ...
 --> Applying Principal Component Analysis...
   >>> Scaling features...
   >>> Keeping 6 components.
   >>> Explained variance ratio: 0.977

In [7]:

# Note that the fetaures are automatically renamed to Component 1, 2, etc...
atom.columns

# Note that the fetaures are automatically renamed to Component 1, 2, etc... atom.columns

Out[7]:

Index(['pca0', 'pca1', 'pca2', 'pca3', 'pca4', 'pca5', 'Rings'], dtype='object')

In [8]:

# Use the plotting methods to see the retained variance ratio
atom.plot_pca()

# Use the plotting methods to see the retained variance ratio atom.plot_pca()

In [9]:

atom.plot_components(figsize=(8, 6))

atom.plot_components(figsize=(8, 6))

Run the pipeline¶

In [10]:

atom.run(
    models=["Tree", "Bag", "ET"],
    metric="mse",
    n_calls=5,
    n_initial_points=2,
    bo_params={"base_estimator": "GBRT"},
    n_bootstrap=5,
)

atom.run( models=["Tree", "Bag", "ET"], metric="mse", n_calls=5, n_initial_points=2, bo_params={"base_estimator": "GBRT"}, n_bootstrap=5, )

Training ========================= >>
Models: Tree, Bag, ET
Metric: neg_mean_squared_error


Running BO for Decision Tree...
| call             |   criterion | splitter | max_depth | min_samples_split | min_samples_leaf | max_features | ccp_alpha | neg_mean_squared_error | best_neg_mean_squared_error |    time | total_time |
| ---------------- | ----------- | -------- | --------- | ----------------- | ---------------- | ------------ | --------- | ---------------------- | --------------------------- | ------- | ---------- |
| Initial point 1  | absolute_.. |   random |        12 |                 8 |               19 |         auto |    0.0161 |                -8.0987 |                     -8.0987 |  0.094s |     0.109s |
| Initial point 2  | absolute_.. |     best |        11 |                 3 |               12 |         None |       0.0 |                -6.7018 |                     -6.7018 |  0.250s |     0.359s |
| Iteration 3      | absolute_.. |     best |        11 |                 4 |                1 |         None |    0.0086 |                -6.8759 |                     -6.7018 |  0.313s |     0.781s |
| Iteration 4      | absolute_.. |   random |        12 |                 3 |               18 |          0.9 |    0.0036 |                -6.9111 |                     -6.7018 |  0.109s |     1.141s |
| Iteration 5      | friedman_.. |   random |         3 |                12 |               19 |         auto |     0.001 |                -6.4336 |                     -6.4336 |  0.047s |     1.287s |
Bayesian Optimization ---------------------------
Best call --> Iteration 5
Best parameters --> {'criterion': 'friedman_mse', 'splitter': 'random', 'max_depth': 3, 'min_samples_split': 12, 'min_samples_leaf': 19, 'max_features': 'auto', 'ccp_alpha': 0.001}
Best evaluation --> neg_mean_squared_error: -6.4336
Time elapsed: 1.428s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -7.4877
Test evaluation --> neg_mean_squared_error: -7.5847
Time elapsed: 0.000s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -7.4566 ± 0.1197
Time elapsed: 0.047s
-------------------------------------------------
Total time: 1.475s


Running BO for Bagging...
| call             | n_estimators | max_samples | max_features | bootstrap | bootstrap_features | neg_mean_squared_error | best_neg_mean_squared_error |    time | total_time |
| ---------------- | ------------ | ----------- | ------------ | --------- | ------------------ | ---------------------- | --------------------------- | ------- | ---------- |
| Initial point 1  |          112 |         0.9 |          0.6 |     False |              False |                -6.5592 |                     -6.5592 |  0.938s |     0.938s |
| Initial point 2  |          131 |         0.5 |          0.5 |     False |              False |                -5.4837 |                     -5.4837 |  0.609s |     1.547s |
| Iteration 3      |          302 |         0.5 |          0.5 |      True |               True |                -6.0919 |                     -5.4837 |  1.094s |     2.735s |
| Iteration 4      |          191 |         0.5 |          0.5 |     False |              False |                -5.3972 |                     -5.3972 |  0.844s |     3.688s |
| Iteration 5      |          217 |         0.5 |          0.5 |     False |              False |                -4.9339 |                     -4.9339 |  0.969s |     4.766s |
Bayesian Optimization ---------------------------
Best call --> Iteration 5
Best parameters --> {'n_estimators': 217, 'max_samples': 0.5, 'max_features': 0.5, 'bootstrap': False, 'bootstrap_features': False}
Best evaluation --> neg_mean_squared_error: -4.9339
Time elapsed: 4.875s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -1.3974
Test evaluation --> neg_mean_squared_error: -5.7349
Time elapsed: 1.278s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -5.9024 ± 0.058
Time elapsed: 5.357s
-------------------------------------------------
Total time: 11.513s


Running BO for Extra-Trees...
| call             | n_estimators |     criterion | max_depth | min_samples_split | min_samples_leaf | max_features | bootstrap | max_samples | ccp_alpha | neg_mean_squared_error | best_neg_mean_squared_error |    time | total_time |
| ---------------- | ------------ | ------------- | --------- | ----------------- | ---------------- | ------------ | --------- | ----------- | --------- | ---------------------- | --------------------------- | ------- | ---------- |
| Initial point 1  |          112 | absolute_er.. |         3 |                 9 |                7 |          0.6 |      True |         0.6 |    0.0117 |                  -8.95 |                       -8.95 |  0.698s |     0.704s |
| Initial point 2  |          369 | absolute_er.. |      None |                 3 |               12 |         None |      True |         0.9 |    0.0216 |                -6.6286 |                     -6.6286 |  6.882s |     7.586s |
| Iteration 3      |          285 | squared_error |      None |                 5 |                3 |         None |      True |         0.9 |    0.0095 |                -6.0219 |                     -6.0219 |  0.664s |     8.376s |
| Iteration 4      |          133 | squared_error |        13 |                11 |                1 |          0.6 |     False |         --- |    0.0004 |                -5.1558 |                     -5.1558 |  0.250s |     8.735s |
| Iteration 5      |           75 | squared_error |         3 |                11 |                2 |          0.8 |     False |         --- |    0.0029 |                -5.9369 |                     -5.1558 |  0.094s |     8.938s |
Bayesian Optimization ---------------------------
Best call --> Iteration 4
Best parameters --> {'n_estimators': 133, 'criterion': 'squared_error', 'max_depth': 13, 'min_samples_split': 11, 'min_samples_leaf': 1, 'max_features': 0.6, 'bootstrap': False, 'ccp_alpha': 0.0004}
Best evaluation --> neg_mean_squared_error: -5.1558
Time elapsed: 9.079s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -3.638
Test evaluation --> neg_mean_squared_error: -5.5804
Time elapsed: 0.285s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -5.6727 ± 0.1028
Time elapsed: 1.172s
-------------------------------------------------
Total time: 10.535s


Final results ==================== >>
Duration: 23.523s
-------------------------------------
Decision Tree --> neg_mean_squared_error: -7.4566 ± 0.1197 ~
Bagging       --> neg_mean_squared_error: -5.9024 ± 0.058 ~
Extra-Trees   --> neg_mean_squared_error: -5.6727 ± 0.1028 ~ !

Analyze the results¶

In [11]:

# Use the errors or residuals plots to check the model performances
atom.plot_residuals()

# Use the errors or residuals plots to check the model performances atom.plot_residuals()

In [12]:

atom.plot_errors()

atom.plot_errors()

In [13]:

# Analyze the relation between the target response and the features
atom.n_jobs = 8  # The method can be slow...
atom.ET.plot_partial_dependence(columns=(0, (2, 3)), figsize=(12, 5))

# Analyze the relation between the target response and the features atom.n_jobs = 8 # The method can be slow... atom.ET.plot_partial_dependence(columns=(0, (2, 3)), figsize=(12, 5))