import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['figure.figsize'] = (8, 8)


## Selecting features for model performance

### Building a diabetes classifier

You'll be using the Pima Indians diabetes dataset to predict whether a person has diabetes using logistic regression. There are 8 features and one target in this dataset.

diabetes_df = pd.read_csv('./dataset/PimaIndians.csv')

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from pprint import pprint

X, y = diabetes_df.iloc[:, :-1], diabetes_df.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

scaler = StandardScaler()
lr = LogisticRegression()

X_train_std = scaler.fit_transform(X_train)

# Fit the logistic regression model on the scaled training data
lr.fit(X_train_std, y_train)

# Scaler the test features
X_test_std = scaler.transform(X_test)

# Predict diabetes presence on the scaled test set
y_pred = lr.predict(X_test_std)

# Print accuracy metrics and feature coefficients
print("{0:.1%} accuracy on test set.".format(accuracy_score(y_test, y_pred)))
pprint(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))

83.9% accuracy on test set.
{'age': 0.21,
'bmi': 0.17,
'diastolic': 0.12,
'family': 0.22,
'glucose': 1.13,
'insulin': 0.11,
'pregnant': 0.29,
'triceps': 0.28}


We get almost 80% accuracy on the test set. Take a look at the differences in model coefficients for the different features.

### Manual Recursive Feature Elimination

Now that we've created a diabetes classifier, let's see if we can reduce the number of features without hurting the model accuracy too much.

On the second line of code the features are selected from the original dataframe. Adjust this selection.

X = diabetes_df[['pregnant', 'glucose', 'triceps',
'insulin', 'bmi', 'family', 'age']]

# Performs a 25-75% train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# Scales features and fits the logistic regression model
lr.fit(scaler.fit_transform(X_train), y_train)

# Calculate the accuracy on the test set and prints coefficients
acc = accuracy_score(y_test, lr.predict(scaler.transform(X_test)))
print("{0: .1%} accuracy on test set.".format(acc))
pprint(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))

 80.6% accuracy on test set.
{'age': 0.35,
'bmi': 0.39,
'family': 0.34,
'glucose': 1.24,
'insulin': 0.2,
'pregnant': 0.05,
'triceps': 0.24}

X = diabetes_df[['glucose', 'triceps', 'bmi', 'family', 'age']]

# Performs a 25-75% train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# Scales features and fits the logistic regression model
lr.fit(scaler.fit_transform(X_train), y_train)

# Calculates the accuracy on the test set and prints coefficients
acc = accuracy_score(y_test, lr.predict(scaler.transform(X_test)))
print("{0:.1%} accuracy on test set.".format(acc))
pprint(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))

79.6% accuracy on test set.
{'age': 0.37, 'bmi': 0.34, 'family': 0.34, 'glucose': 1.13, 'triceps': 0.25}

X = diabetes_df[['glucose']]

# Performs a 25-75% train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# Scales features and fits the logistic regression model to the data
lr.fit(scaler.fit_transform(X_train), y_train)

# Calculates the accuracy on the test set and prints coefficients
acc = accuracy_score(y_test, lr.predict(scaler.transform(X_test)))
print("{0:.1%} accuracy on test set.".format(acc))
print(dict(zip(X.columns, abs(lr.coef_[0]).round(2))))

75.5% accuracy on test set.
{'glucose': 1.28}


### Automatic Recursive Feature Elimination

Now let's automate this recursive process. Wrap a Recursive Feature Eliminator (RFE) around our logistic regression estimator and pass it the desired number of features.

X, y = diabetes_df.iloc[:, :-1], diabetes_df.iloc[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

lr = LogisticRegression()

# Fit the scaler on the training features and transform these in one go
X_train_std = scaler.fit_transform(X_train)

# Fit the logistic regression model on the scaled training data
lr.fit(X_train_std, y_train)

# Scaler the test features
X_test_std = scaler.transform(X_test)

from sklearn.feature_selection import RFE

# Create the RFE a LogisticRegression estimator and 3 features to select
rfe = RFE(estimator=LogisticRegression(), n_features_to_select=3, verbose=1)

# Fits the eliminator to the data
rfe.fit(X_train_std, y_train)

# Print the features and their ranking (high = dropped early on)
print(dict(zip(X.columns, rfe.ranking_)))

# Print the features that are not elimiated
print(X.columns[rfe.support_])

# CAlculates the test set accuracy
acc = accuracy_score(y_test, rfe.predict(X_test_std))
print("{0:.1%} accuracy on test set.".format(acc))

Fitting estimator with 8 features.
Fitting estimator with 7 features.
Fitting estimator with 6 features.
Fitting estimator with 5 features.
Fitting estimator with 4 features.
{'pregnant': 3, 'glucose': 1, 'diastolic': 4, 'triceps': 6, 'insulin': 5, 'bmi': 2, 'family': 1, 'age': 1}
Index(['glucose', 'family', 'age'], dtype='object')
75.4% accuracy on test set.


## Tree-based feature selection

• Random forest classifier

### Building a random forest model

You'll again work on the Pima Indians dataset to predict whether an individual has diabetes. This time using a random forest classifier. You'll fit the model on the training data after performing the train-test split and consult the feature importance values.

from sklearn.ensemble import RandomForestClassifier

# Perform a 75% training and 25% test data split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

# Fit the random forest model to the training data
rf = RandomForestClassifier(random_state=0)
rf.fit(X_train, y_train)

# Calculate the accuracy
acc = accuracy_score(y_test, rf.predict(X_test))

# Print the importances per feature
pprint(dict(zip(X.columns, rf.feature_importances_.round(2))))

# Print accuracy
print("{0:.1%} accuracy on test set.".format(acc))

{'age': 0.13,
'bmi': 0.12,
'diastolic': 0.09,
'family': 0.12,
'glucose': 0.25,
'insulin': 0.14,
'pregnant': 0.07,
'triceps': 0.09}
79.6% accuracy on test set.


### Random forest for feature selection

Now lets use the fitted random model to select the most important features from our input dataset X.

mask = rf.feature_importances_ > 0.15

# Apply the mask to the feature dataset X

# Prints out the selected column names
print(reduced_X.columns)

[False  True False False False False False False]
Index(['glucose'], dtype='object')


### Recursive Feature Elimination with random forests

You'll wrap a Recursive Feature Eliminator around a random forest model to remove features step by step. This method is more conservative compared to selecting features after applying a single importance threshold. Since dropping one feature can influence the relative importances of the others.

rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=2, verbose=1)

# Fit the model to the training data
rfe.fit(X_train, y_train)

# Create a mask using an attribute of rfe

# Apply the mask to the feature dataset X and print the result
print(reduced_X.columns)

Fitting estimator with 8 features.
Fitting estimator with 7 features.
Fitting estimator with 6 features.
Fitting estimator with 5 features.
Fitting estimator with 4 features.
Fitting estimator with 3 features.
Index(['glucose', 'bmi'], dtype='object')

rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=2, step=2, verbose=1)

# Fit the model to the training data
rfe.fit(X_train, y_train)

# Create a mask using an attribute of rfe

# Apply the mask to the feature dataset X and print the result
print(reduced_X.columns)

Fitting estimator with 8 features.
Fitting estimator with 6 features.
Fitting estimator with 4 features.
Index(['glucose', 'age'], dtype='object')


Compared to the quick and dirty single threshold method from the previous exercise one of the selected features is different.

## Regularized linear regression

• Loss function: Mean Squared Error
• Adding regularization $$\text{MSE} + \overbrace{\alpha(\vert \beta_1 \vert + \vert \beta_2 \vert + \vert \beta_3 \vert)}^{\text{Regularization term}}$$
• MSE tries to make model accurate
• Regularization term tries to make model simple
• $\alpha$, when it's too low, the model might overfit. when it's too high, the model might become too simple and inaccurate. One linear model that includes this type of regularization is called Lasso, for Least Absolute Shrinkage and Selection.

### Creating a LASSO regressor

You'll be working on the numeric ANSUR body measurements dataset to predict a persons Body Mass Index (BMI) using the Lasso() regressor. BMI is a metric derived from body height and weight but those two features have been removed from the dataset to give the model a challenge.

You'll standardize the data first using the StandardScaler() that has been instantiated for you as scaler to make sure all coefficients face a comparable regularizing force trying to bring them down.

ansur_male = pd.read_csv('./dataset/ANSUR_II_MALE.csv')

ansur_df = ansur_male
# unused columns in the dataset
unused = ['Gender', 'Branch', 'Component', 'BMI_class', 'Height_class', 'weight_kg', 'stature_m']

# Drop the non-numeric columns from df
ansur_df.drop(unused, axis=1, inplace=True)

X = ansur_df.drop('BMI', axis=1)
y = ansur_df['BMI']

scaler = StandardScaler()

from sklearn.linear_model import Lasso

# Set the test size to 30% to get a 70-30% train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# Fit the scaler on the training features and transform these in one go
X_train_std = scaler.fit_transform(X_train)

# Create the Lasso model
la = Lasso()

# Fit it to the standardized training data
la.fit(X_train_std, y_train)

Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)

### Lasso model results

Now that you've trained the Lasso model, you'll score its predictive capacity ($R^2$) on the test set and count how many features are ignored because their coefficient is reduced to zero.

X_test_std = scaler.transform(X_test)

# Calculate the coefficient of determination (R squared) on X_test_std
r_squared = la.score(X_test_std, y_test)
print("The model can predict {0:.1%} of the variance in the test set.".format(r_squared))

# Create a list that has True values when coefficients equal 0
zero_coef = la.coef_ == 0

# Calculate how many features have a zero coefficient
n_ignored = sum(zero_coef)
print("The model has ignored {} out of {} features.".format(n_ignored, len(la.coef_)))

The model can predict 84.7% of the variance in the test set.
The model has ignored 82 out of 91 features.


We can predict almost 85% of the variance in the BMI value using just 9 out of 91 of the features. The $R^2$ could be higher though.

Your current Lasso model has an $R^2$ score of 84.7%. When a model applies overly powerful regularization it can suffer from high bias, hurting its predictive power.

Let's improve the balance between predictive power and model simplicity by tweaking the alpha parameter.

alpha_list = [1, 0.5, 0.1, 0.01]
max_r = 0
max_alpha = 0
for alpha in alpha_list:
# Find the highest alpha value with R-squared above 98%
la = Lasso(alpha=alpha, random_state=0)

# Fits the model and calculates performance stats
la.fit(X_train_std, y_train)
r_squared = la.score(X_test_std, y_test)
n_ignored_features = sum(la.coef_ == 0)

# Print peformance stats
print("The model can predict {0:.1%} of the variance in the test set.".format(r_squared))
print("{} out of {} features were ignored.".format(n_ignored_features, len(la.coef_)))
if r_squared > 0.98:
max_r = r_squared
max_alpha = alpha
break
print("Max R-squared: {}, alpha: {}".format(max_r, max_alpha))

The model can predict 84.7% of the variance in the test set.
82 out of 91 features were ignored.
The model can predict 93.8% of the variance in the test set.
79 out of 91 features were ignored.
The model can predict 98.3% of the variance in the test set.
64 out of 91 features were ignored.
Max R-squared: 0.9828190248586458, alpha: 0.1


## Combining feature selectors

### Creating a LassoCV regressor

You'll be predicting biceps circumference on a subsample of the male ANSUR dataset using the LassoCV() regressor that automatically tunes the regularization strength (alpha value) using Cross-Validation.

X = ansur_df[['acromialheight', 'axillaheight', 'bideltoidbreadth', 'buttockcircumference', 'buttockkneelength', 'buttockpopliteallength', 'cervicaleheight', 'chestcircumference', 'chestheight',
'lateralfemoralepicondyleheight', 'lateralmalleolusheight', 'neckcircumferencebase', 'radialestylionlength', 'shouldercircumference', 'shoulderelbowlength', 'sleeveoutseam',
'thighcircumference', 'thighclearance', 'verticaltrunkcircumferenceusa', 'waistcircumference', 'waistdepth', 'wristheight', 'BMI']]
y = ansur_df['bicepscircumferenceflexed']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

from sklearn.linear_model import LassoCV

# Create and fit the LassoCV model on the training set
lcv = LassoCV()
lcv.fit(X_train, y_train)
print('Optimal alpha = {0:.3f}'.format(lcv.alpha_))

# Calculate R squared on the test set
r_squared = lcv.score(X_test, y_test)
print('The model explains {0:.1%} of the test set variance'.format(r_squared))

# Create a mask for coefficients not equal to zero

Optimal alpha = 0.035
The model explains 85.6% of the test set variance
24 features out of 32 selected


### Ensemble models for extra votes

The LassoCV() model selected 24 out of 32 features. Not bad, but not a spectacular dimensionality reduction either. Let's use two more models to select the 10 features they consider most important using the Recursive Feature Eliminator (RFE).

from sklearn.feature_selection import RFE

# Select 10 features with RFE on a GradientBoostingRegressor, drop 3 features on each step
n_features_to_select=10, step=3, verbose=1)
rfe_gb.fit(X_train, y_train)

# Calculate the R squared on the test set
r_squared = rfe_gb.score(X_test, y_test)
print('The model can explain {0:.1%} of the variance in the test set'.format(r_squared))

# Assign the support array to gb_mask

Fitting estimator with 32 features.
Fitting estimator with 29 features.
Fitting estimator with 26 features.
Fitting estimator with 23 features.
Fitting estimator with 20 features.
Fitting estimator with 17 features.
Fitting estimator with 14 features.
Fitting estimator with 11 features.
The model can explain 83.3% of the variance in the test set

from sklearn.ensemble import RandomForestRegressor

# Select 10 features with RFE on a RandomForestRegressor, drop 3 features on each step
rfe_rf = RFE(estimator=RandomForestRegressor(),
n_features_to_select=10, step=3, verbose=1)
rfe_rf.fit(X_train, y_train)

# Calculate the R squared on the test set
r_squared = rfe_rf.score(X_test, y_test)
print('The model can explain {0:.1%} of the variance in the test set'.format(r_squared))

# Assign the support array to rf_mask

Fitting estimator with 32 features.
Fitting estimator with 29 features.
Fitting estimator with 26 features.
Fitting estimator with 23 features.
Fitting estimator with 20 features.
Fitting estimator with 17 features.
Fitting estimator with 14 features.
Fitting estimator with 11 features.
The model can explain 82.3% of the variance in the test set


Inluding the Lasso linear model from the previous exercise, we now have the votes from 3 models on which features are important.

### Combining 3 feature selectors

We'll combine the votes of the 3 models you built in the previous exercises, to decide which features are important into a meta mask. We'll then use this mask to reduce dimensionality and see how a simple linear regressor performs on the reduced dataset.

votes = np.sum([lcv_mask, rf_mask, gb_mask], axis=0)

# Create a mask for features selected by all 3 models

# Apply the dimensionality reduction on X
print(X_reduced.columns)

[0 1 3 3 0 1 1 3 1 1 1 3 1 1 1 0 0 1 0 1 1 1 3 3 0 3 2 2 1 2 0 3]
[False False  True  True False False False  True False False False  True
False False False False False False False False False False  True  True
False  True False False False False False  True]
'forearmcircumferenceflexed', 'shouldercircumference',
'shoulderelbowlength', 'thighcircumference', 'BMI'],
dtype='object')

from sklearn.linear_model import LinearRegression

lm = LinearRegression()

# Plug the reduced data into a linear regression pipeline
X_train, X_test, y_train, y_test = train_test_split(X_reduced, y, test_size=0.3, random_state=0)
lm.fit(scaler.fit_transform(X_train), y_train)
r_squared = lm.score(scaler.transform(X_test), y_test)
print('The model can explain {0:.1%} of the variance in the test set using {1:} features.'.format(r_squared, len(lm.coef_)))

The model can explain 84.0% of the variance in the test set using 8 features.


Using the votes from 3 models you were able to select just 7 features that allowed a simple linear model to get a high accuracy!