I am using XGboost for a binary prediction problem. I tested my model with several features and had some good results.
After adding one feature to the model and calculating the feature importance. The importance of this feature showed to be very high and far superior to other features.
However, when testing the model the test score drops considerably.
Is there an explanation for this kind of behaviour ?
There are at least a few ways to run feature importance experiments.
# Let's load the packages
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.inspection import permutation_importance
import shap
from matplotlib import pyplot as plt
plt.rcParams.update({'figure.figsize': (12.0, 8.0)})
plt.rcParams.update({'font.size': 14})
boston = load_boston()
X = pd.DataFrame(boston.data, columns=boston.feature_names)
y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=12)
rf = RandomForestRegressor(n_estimators=100)
rf.fit(X_train, y_train)
# 1
rf.feature_importances_
plt.barh(boston.feature_names, rf.feature_importances_)
sorted_idx = rf.feature_importances_.argsort()
plt.barh(boston.feature_names[sorted_idx], rf.feature_importances_[sorted_idx])
plt.xlabel("Random Forest Feature Importance")
# 2
perm_importance = permutation_importance(rf, X_test, y_test)
sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(boston.feature_names[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")
# 3
explainer = shap.TreeExplainer(rf)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, plot_type="bar")
Also, you can certainly add more data into your model. Models, almost without exception, produce more accurate results when they 'see' more data. Finally, you can always test other models on your dataset and see how they perform. Today at work I tested an XGboost model and a RandomForestRegressor model. I expected the former to perform better, but the latter actually performed much better. It's almost impossible to guess which model will perform better over any given dataset, you have to try multiple models, check the predictive capabilities of each, and pick the one (or maybe two) that performs the best. Having said that, you can try something like this.
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster, datasets
from sklearn.neighbors import kneighbors_graph
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
np.random.seed(0)
pd.set_option('display.max_columns', 500)
#df = pd.read_csv('C:\\your_path_here\\test.csv')
#print('done!')
#df = df[:10000]
#df = df.fillna(0)
#df = df.dropna()
X = df[['RatingScore',
'Par',
'Term',
'TimeToMaturity',
'LRMScore',
'Coupon',
'Price']]
#select your target variable
y = df[['Spread']]
#train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])
colors = np.hstack([colors] * 20)
clustering_names = [
'MiniBatchKMeans', 'AffinityPropagation', 'MeanShift',
'SpectralClustering', 'Ward', 'AgglomerativeClustering',
'DBSCAN', 'Birch']
plt.figure(figsize=(len(clustering_names) * 2 + 3, 9.5))
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
hspace=.01)
plot_num = 1
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# estimate bandwidth for mean shift
bandwidth = cluster.estimate_bandwidth(X, quantile=0.3)
# connectivity matrix for structured Ward
connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False)
# make connectivity symmetric
connectivity = 0.5 * (connectivity + connectivity.T)
# create clustering estimators
ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
two_means = cluster.MiniBatchKMeans(n_clusters=2)
ward = cluster.AgglomerativeClustering(n_clusters=2, linkage='ward',
connectivity=connectivity)
spectral = cluster.SpectralClustering(n_clusters=2,
eigen_solver='arpack',
affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.2)
affinity_propagation = cluster.AffinityPropagation(damping=.9,
preference=-200)
average_linkage = cluster.AgglomerativeClustering(
linkage="average", affinity="cityblock", n_clusters=2,
connectivity=connectivity)
birch = cluster.Birch(n_clusters=2)
clustering_algorithms = [
two_means, affinity_propagation, ms, spectral, ward, average_linkage,
dbscan, birch]
for name, algorithm in zip(clustering_names, clustering_algorithms):
# predict cluster memberships
t0 = time.time()
algorithm.fit(X)
t1 = time.time()
if hasattr(algorithm, 'labels_'):
y_pred = algorithm.labels_.astype(np.int)
else:
y_pred = algorithm.predict(X)
# plot
plt.subplot(4, len(clustering_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
if hasattr(algorithm, 'cluster_centers_'):
centers = algorithm.cluster_centers_
center_colors = colors[:len(centers)]
plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
plt.xlim(-2, 2)
plt.ylim(-2, 2)
plt.xticks(())
plt.yticks(())
plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
transform=plt.gca().transAxes, size=15,
horizontalalignment='right')
plot_num += 1
plt.show()
Finally, consider looping through several regression, or classification, models in one go, and getting the results for each.
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
from sklearn import linear_model
import statsmodels.api as sm
X = X
y = y
# Note the difference in argument order
model = sm.OLS(y, X).fit()
predictions = model.predict(X) # make the predictions by the model
# Print out the statistics
model.summary()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import SGDRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.linear_model import TweedieRegressor
from sklearn.linear_model import PoissonRegressor
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.svm import LinearSVR
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
regressors = [
LinearRegression(),
SGDRegressor(),
KNeighborsRegressor(),
DecisionTreeRegressor(),
RandomForestRegressor(),
GradientBoostingRegressor(),
TweedieRegressor(),
PoissonRegressor(),
Ridge(),
Lasso()
]
import pandas as pd
# Logging for Visual Comparison
log_cols=["Regressor", "RMSE", "MAE"]
log = pd.DataFrame(columns=log_cols)
for reg in regressors:
reg.fit(X_train, y_train)
name = reg.__class__.__name__
print(reg.score(X_test, y_test))
y_pred = reg.predict(X_test)
lr_mse = mean_squared_error(y_pred, y_test)
lr_rmse = np.sqrt(lr_mse)
print(name + ' RMSE: %.4f' % lr_rmse)
lin_mae = mean_absolute_error(y_pred, y_test)
print(name + ' MAE: %.4f' % lin_mae)
log_entry = pd.DataFrame([[name, lr_rmse, lin_mae]], columns=log_cols)
log = log.append(log_entry)
print("="*30)
import seaborn as sns
import matplotlib as plt
sns.set_color_codes("muted")
sns.barplot(x='RMSE', y='Regressor', data=log, color="b")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, log_loss
from sklearn.neighbors import KNeighborsClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.datasets import load_iris
iris = load_iris()
iris
# Step 2: Separating the data into dependent and independent variables
X = iris.data[:, :2] # we only take the first two features.
y = iris.target
# Step 3: Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
classifiers = [
GaussianNB(),
MLPClassifier(),
KNeighborsClassifier(),
GaussianProcessClassifier(),
DecisionTreeClassifier(),
RandomForestClassifier(),
AdaBoostClassifier(),
GradientBoostingClassifier(),
QuadraticDiscriminantAnalysis()]
import pandas as pd
# Logging for Visual Comparison
log_cols=["Classifier", "Accuracy"]
log = pd.DataFrame(columns=log_cols)
for clf in classifiers:
clf.fit(X_train, y_train)
name = clf.__class__.__name__
print("="*30)
print(name)
print('****Results****')
train_predictions = clf.predict(X_test)
acc = accuracy_score(y_test, train_predictions)
print("Accuracy: {:.4%}".format(acc))
log_entry = pd.DataFrame([[name, acc*100]], columns=log_cols)
log = log.append(log_entry)
print("="*30)
import seaborn as sns
import matplotlib as plt
sns.set_color_codes("muted")
sns.barplot(x='Accuracy', y='Classifier', data=log, color="b")
The full error is this. I am not sure how to fix it. I'm trying to predict the link between gender and aggresiveness in tweets.
(https://i.stack.imgur.com/T4Ual.png)
This is the whole script
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#De specifikke, vi ved vi kommer til at bruge
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB #Gør at man kan have mere end 2 classes
data = pd.read_csv('/work/90301/Individual project/TheClimateChangeTwitterDataset.csv')
#corpus=data['text']
#corpus=text.loc[:,['aggressiveness', 'gender']]
cv=CountVectorizer() #Take some text and turn it into a matrix
X = cv.fit_transform(data.values).toarray()
#x = X['aggressiveness'].values
#y = X['gender'].values
y=data['gender'].values
print(X.shape)
print(y.shape)
X_train,X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
#Instantiate and train Naive Bayes
classifier = MultinomialNB(fit_prior=True)
classifier.fit(X_train, y_train)
#test model
y_pred = classifier.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(f'Relative accuracy: {accuracy_score(y_test, y_pred)}')
print(f'Accuracy in instances: {accuracy_score(y_test, y_pred, normalize=False)}')
#Infer the label (spam/ham) of a message
aggressiveness=[corpus]
#print(email)
aggressiveness_array = cv.transform(aggressiveness).toarray()
print(classifier.predict(aggressiveness_array))
I am trying to get SHAP values for a Gaussian Processes Regression (GPR) model using SHAP library. However, all SHAP values are zero. I am using the example in the official documentation. I only changed the model to GPR.
import sklearn
from sklearn.model_selection import train_test_split
import numpy as np
import shap
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern, WhiteKernel, ConstantKernel
shap.initjs()
X,y = shap.datasets.diabetes()
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# rather than use the whole training set to estimate expected values, we summarize with
# a set of weighted kmeans, each weighted by the number of points they represent.
X_train_summary = shap.kmeans(X_train, 10)
kernel = Matern(length_scale=2, nu=3/2) + WhiteKernel(noise_level=1)
gp = GaussianProcessRegressor(kernel)
gp.fit(X_train, y_train)
# explain all the predictions in the test set
explainer = shap.KernelExplainer(gp.predict, X_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
Running the above code gives the following plot:
When I use Neural Network or Linear Regression, the above code works fine without problem.
If you have any idea how to solve this issue, please let me know.
Your model doesn't predict anything:
plt.scatter(y_test, gp.predict(X_test));
Train your model properly, like below:
plt.scatter(y_test, gp.predict(X_test));
and you're fine to go:
explainer = shap.KernelExplainer(gp.predict, X_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
Full reproducible example:
import sklearn
from sklearn.model_selection import train_test_split
import numpy as np
import shap
import time
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import WhiteKernel, DotProduct
X,y = shap.datasets.diabetes()
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=0)
X_train_summary = shap.kmeans(X_train, 10)
kernel = DotProduct() + WhiteKernel()
gp = GaussianProcessRegressor(kernel)
gp.fit(X_train, y_train)
explainer = shap.KernelExplainer(gp.predict, X_train_summary)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
Try this code:
kernel = 1.0 * Matern(length_scale=1.0, nu=2.5) + \
WhiteKernel(noise_level=10**-1,noise_level_bounds=(10**-1, 10**1))
model = GaussianProcessRegressor(kernel=kernel,
optimizer='fmin_l_bfgs_b',random_state=123)
explainer = shap.Explainer(model.predict,X_train)
shap_values = explainer.shap_values(X_train)
shap.plots.bar(shap_values) ## bar plot
shap.summary_plot(shap_values, X_train,show=False) ## summary
I'm doing the "Hello world" in machine learning, using the Iris dataset. I already have an acceptable result for the entry of this model, I am using 80% of the information to train it and the remaining 20% to do the validation. I am using 6 prediction algorithms, which work well.
but I have a problem, how can I insert new information so that it is analyzed? How do I insert the characteristics of a flower and tell me the type of iris it is? Either: Iris-setosa, Iris-versicolor or Iris-virginica?
# Load libraries
import pandas
from pandas.plotting import scatter_matrix
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
# Load dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url, names=names)
#######Evaluate Some Algorithms########
#Create a Validation Dataset
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
Y = array[:,4]
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
########Build Models########
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
########Make Predictions########
print('######## Make Predictions ########')
# Make predictions on validation dataset
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
predictions = knn.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
I think you can follow this other post to save your model, and after you can load him and pass new data and make some predictions.
Remember to set the data to same input shape as used during training.
import cPickle
# save the classifier
with open('my_dumped_classifier.pkl', 'wb') as fid:
cPickle.dump(gnb, fid)
# load it again
with open('my_dumped_classifier.pkl', 'rb') as fid:
gnb_loaded = cPickle.load(fid)
# make predictions
In sklearn, when using SGDCLassifier for elastic-net logistic regression, the predict_proba function returns the same thing as the predict function.
AKA the code below (with X and y the predictors and binary label respectively) returns True:
EN = sklearn.linear_model.SGDClassifier(loss='log', penalty='elasticnet',
alpha=0.0001, l1_ratio=0.15)
EN.fit(X[train], y[train])
numpy.all(EN.predict(X[test]) == EN.predict_proba(X[test])[:,1])
How to obtain probability values?
It seems that the sklearn version is the problem. You need to upgrade to 0.18.2.
Example using iris data:
from sklearn.datasets import load_iris
from sklearn.linear import model.SGDClassifier
from sklearn.model_selection import train_test_split
import numpy
import sklearn
data = load_iris()
x = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 42)
EN = SGDClassifier(loss='log', penalty='elasticnet', alpha=0.0001, l1_ratio=0.15)
EN.fit(X_train, y_train)
numpy.all(EN.predict(X_test) == EN.predict_proba(X_test)[:,1])
sklearn.__version__
Result
False
'0.18.2'
So with sklearn 0.18.2 it works fine.