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, warnings=False, random_state=42)

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

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

Dataset stats ==================== >>
Shape: (4177, 9)
Scaled: False
Categorical features: 1 (12.5%)
Outlier values: 192 (0.6%)
-------------------------------------
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...
   >>> Total explained variance: 0.976

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]:

['Component 1',
 'Component 2',
 'Component 3',
 'Component 4',
 'Component 5',
 'Component 6',
 'Rings']

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  | absolut.. |   random |         7 |                 8 |               19 |         None |     0.016 |                 -7.725 |                      -7.725 |  0.106s |     0.112s |
| Initial point 2  | absolut.. |     best |         6 |                 3 |               12 |          0.9 |       0.0 |                -8.3285 |                      -7.725 |  0.223s |     0.527s |
| Iteration 3      | absolut.. |   random |         7 |                14 |               16 |         None |     0.028 |                -6.8909 |                     -6.8909 |  0.104s |     0.808s |
| Iteration 4      | friedma.. |     best |         6 |                19 |               16 |          0.9 |     0.031 |                -6.6381 |                     -6.6381 |  0.068s |     1.055s |
| Iteration 5      | friedma.. |   random |      None |                19 |               13 |          0.7 |     0.029 |                -6.8263 |                     -6.6381 |  0.064s |     1.294s |

Results for Decision Tree:         
Bayesian Optimization ---------------------------
Best call --> Iteration 4
Best parameters --> {'criterion': 'friedman_mse', 'splitter': 'best', 'max_depth': 6, 'min_samples_split': 19, 'min_samples_leaf': 16, 'max_features': 0.9, 'ccp_alpha': 0.031}
Best evaluation --> neg_mean_squared_error: -6.6381
Time elapsed: 1.471s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -6.1632
Test evaluation --> neg_mean_squared_error: -5.4816
Time elapsed: 0.015s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -5.7348 ± 0.2036
Time elapsed: 0.062s
-------------------------------------------------
Total time: 1.549s




Running BO for Bagging Regressor...
| 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 |                 -6.007 |                      -6.007 |  0.897s |     0.901s |
| Initial point 2  |          131 |         0.5 |          0.5 |     False |              False |                 -7.047 |                      -6.007 |  0.616s |     1.578s |
| Iteration 3      |           50 |         0.9 |          0.6 |     False |               True |                -5.3357 |                     -5.3357 |  0.449s |     2.189s |
| Iteration 4      |           74 |         0.5 |          0.5 |     False |               True |                -6.1811 |                     -5.3357 |  0.370s |     2.728s |
| Iteration 5      |           18 |         0.8 |          0.6 |      True |              False |                -6.3648 |                     -5.3357 |  0.145s |     3.035s |

Results for Bagging Regressor:         
Bayesian Optimization ---------------------------
Best call --> Iteration 3
Best parameters --> {'n_estimators': 50, 'max_samples': 0.9, 'max_features': 0.6, 'bootstrap': False, 'bootstrap_features': True}
Best evaluation --> neg_mean_squared_error: -5.3357
Time elapsed: 3.207s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -0.0867
Test evaluation --> neg_mean_squared_error: -4.9533
Time elapsed: 0.510s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -5.1436 ± 0.0702
Time elapsed: 2.113s
-------------------------------------------------
Total time: 5.833s




Running BO for Extra-Trees...
| call             | n_estimators | criterion | max_depth | min_samples_split | min_samples_leaf | max_features | bootstrap | ccp_alpha | max_samples | neg_mean_squared_error | best_neg_mean_squared_error |    time | total_time |
| ---------------- | ------------ | --------- | --------- | ----------------- | ---------------- | ------------ | --------- | --------- | ----------- | ---------------------- | --------------------------- | ------- | ---------- |
| Initial point 1  |          112 | absolut.. |         1 |                 9 |                7 |          0.6 |      True |     0.016 |         0.6 |               -10.2144 |                    -10.2144 |  0.389s |     0.395s |
| Initial point 2  |          369 | absolut.. |      None |                 3 |               12 |          0.9 |      True |     0.035 |         0.8 |                -9.4213 |                     -9.4213 |  4.798s |     5.259s |
| Iteration 3      |          385 | squared.. |      None |                 6 |               18 |          0.9 |     False |      0.02 |         --- |                -5.5509 |                     -5.5509 |  0.533s |     5.979s |
| Iteration 4      |          425 | squared.. |         1 |                20 |               19 |          0.7 |     False |     0.016 |         --- |                -9.1701 |                     -5.5509 |  0.329s |     6.497s |
| Iteration 5      |          445 | squared.. |      None |                 7 |               20 |          0.6 |     False |     0.004 |         --- |                -6.9472 |                     -5.5509 |  0.440s |     7.228s |

Results for Extra-Trees:         
Bayesian Optimization ---------------------------
Best call --> Iteration 3
Best parameters --> {'n_estimators': 385, 'criterion': 'squared_error', 'max_depth': None, 'min_samples_split': 6, 'min_samples_leaf': 18, 'max_features': 0.9, 'bootstrap': False, 'ccp_alpha': 0.02}
Best evaluation --> neg_mean_squared_error: -5.5509
Time elapsed: 7.428s
Fit ---------------------------------------------
Train evaluation --> neg_mean_squared_error: -6.1021
Test evaluation --> neg_mean_squared_error: -5.0002
Time elapsed: 0.620s
Bootstrap ---------------------------------------
Evaluation --> neg_mean_squared_error: -4.9113 ± 0.0278
Time elapsed: 3.026s
-------------------------------------------------
Total time: 11.074s


Final results ==================== >>
Duration: 18.457s
-------------------------------------
Decision Tree     --> neg_mean_squared_error: -5.7348 ± 0.2036 ~
Bagging Regressor --> neg_mean_squared_error: -5.1436 ± 0.0702 ~
Extra-Trees       --> neg_mean_squared_error: -4.9113 ± 0.0278 ~ !

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 [14]:

# 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))