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nursery_prediction.py
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nursery_prediction.py
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#This code predicts the ranking of a nursery school admission application into different classes. It is a multi-class classification problem.
import warnings
warnings.filterwarnings('ignore')
# Import Libraries
from numpy import mean,std
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
from sklearn.model_selection import RepeatedStratifiedKFold,cross_val_score
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score,mean_squared_error
from sklearn.model_selection import RandomizedSearchCV
from mlxtend.evaluate import bias_variance_decomp
from sklearn.ensemble import BaggingClassifier,RandomForestClassifier,VotingClassifier
from sklearn.preprocessing import LabelEncoder,LabelBinarizer
from sklearn.ensemble import IsolationForest
from imblearn.over_sampling import SMOTE
import category_encoders as ce
from sklearn.metrics import precision_recall_fscore_support
from scipy import interp
from sklearn.metrics import roc_curve, auc
# Importing or Loading dataset
#data = "C:/Users/Gabriel/Desktop/nursery_data.csv"
df = pd.read_csv(data, delimiter=',')
df = df.iloc[0:6000]
# Inspect data
print('Nursery Data without imbalance and Outliers')
print('Inspect Data')
#print(df.head(20).to_string())
print('\n')
# Through inspection, the nursery dataset is a dataset of categorical type with no missing values but
# has imbalance multiple classes.
# Check data shape
print('Data Shape----------:',df.shape)
# Check Data Types
print('Check the Data Types----------')
print(df.info())
print('\n')
# Check missing values in the data
df3 = df.isnull().sum()
print('Missing values in each feature \n:-------------------------------')
print(df3) # NOTE: There are no missing values in the data
df['target'] = df['target'].replace(to_replace=['recommend','very_recom'],
value='recommend')
print(df.head(20).to_string())
# Separate feature vectors from target labels
X = df.drop('target',axis=1)
#print(X.to_string())
y = df['target'].copy()
# Group data by class to see how the samples are distributed between the two classes
grp_data = df.groupby(y).size()
print(grp_data)
y.hist()
plt.title('Imbalanced Classes for Multi-class Classification')
plt.show()
# Transform categorical attributes to numeric attributes
feature_enc = ce.OneHotEncoder(handle_unknown='ignore', use_cat_names=True)
X = feature_enc.fit_transform(X)
#print(X.to_string())
# Check the descriptive statistics of each feature
for i in X:
print(X[i].describe())
# As it can be seen from the statistical description of the data, the max value and std for some
# features are far from the mean given the median value. This shows the presence of outliers.
# Convert the Dataframe to Numpy Arrays
X = X.values
y = y.values
#print(y)
# Encoding attributes or Label Encoding: Transform the labels + to 0 and - to 1
enc = LabelEncoder()
y = enc.fit_transform(y)
print('Encoded Labels')
print(y)
# DATA PREPARATION ENDS HERE---------------------------------------------------------------------
# Split the dataset into the Training set and Test set-------------------------
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)
# Check Outliers-------------------------------------------
# identify outliers using Isolation Forest in the training dataset
# The data has been transformed between a range of 0 and 1. So there is no need to check for outliers
# identify outliers using Isolation Forest in the training dataset
iso = IsolationForest(contamination=0.1)
# Contamination argument is used to help estimate the number of outliers in the dataset.
# This is a value between 0.0 and 0.5 and by default is set to 0.1.
outl = iso.fit_predict(X_train)
# select all rows that are not outliers
remove_outl = outl != -1
X_train, y_train = X_train[remove_outl, :], y_train[remove_outl]
# summarize the shape of the updated training dataset
print('New data without outliers \n')
print(X_train.shape, y_train.shape)
# Handling Class Imbalance: Ensure that there are no synthetic data in the test data or validation data
# Synthetic data can only be in the training data
# Check Class Distribution for Imbalance: SMOTE and bagging ensemble methods are used to handle the class imbalance problem
smt = SMOTE(random_state=42)
X_train, y_train = smt.fit_resample(X_train, y_train)
# View the training data after oversampling
print('Viewing data after oversampling using resample SMOTE')
X_train = pd.DataFrame(X_train)
print(X_train.shape)
# Check the shape of the balanced feature vectors
print('New Feature vector shape:',X_train.shape)
print('New Class shape:',y_train.shape)
# Visualize balanced classes
plt.hist(y_train)
plt.title('Balanced Class Distribution ')
plt.show()
# Performing feature normalization or standardization-----------------------------------
# The range of values for the attributes are of the same range
print('\n')
# MODEL DEVELOPMENT BEGINS
print('# MODEL DEVELOPMENT BEGINS')
# Cross validation of 10 folds and 5 runs
cv_method = RepeatedStratifiedKFold(n_splits=10, n_repeats=5, random_state=42)
# Hyperparameter Optimization
# get a voting ensemble of models
def NB_Ensemble():
# Develop NB ensemble
models = list()
models.append(('NB1', GaussianNB(var_smoothing=1e-9)))
models.append(('NB2', MultinomialNB(alpha=1.0)))
models.append(('NB3', BernoulliNB(alpha=1.0)))
models.append(('NB4', GaussianNB(var_smoothing=1e-5)))
models.append(('NB5', MultinomialNB(alpha=0.5)))
# define the voting ensemble
NBE = VotingClassifier(estimators=models, voting='soft')
return NBE
# define the base models
def kNN_Ensemble():
# Develop kNN Ensemble
models = list()
models.append(('KNN1', KNeighborsClassifier(n_neighbors=1,p=2)))
models.append(('kNN3', KNeighborsClassifier(n_neighbors=3, p=5)))
models.append(('kNN5', KNeighborsClassifier(n_neighbors=5, p=2)))
models.append(('kNN7', KNeighborsClassifier(n_neighbors=7, p=1)))
models.append(('kNN9', KNeighborsClassifier(n_neighbors=9, p=5)))
# define the voting ensemble
kNNE = VotingClassifier(estimators=models, voting='soft')
return kNNE
def DT_Ensemble():
# Develop DT Ensemble
models = list()
models.append(('DT1', DecisionTreeClassifier(max_depth=5,criterion='entropy',splitter='best')))
models.append(('DT2', DecisionTreeClassifier(max_depth=10,criterion='gini',splitter='best')))
models.append(('DT3', DecisionTreeClassifier(max_depth=15,criterion='entropy',splitter='random')))
models.append(('DT4', DecisionTreeClassifier(max_depth=20,criterion='gini',splitter='random')))
models.append(('DT5', DecisionTreeClassifier(max_depth=25,criterion='gini',splitter='best')))
# define the voting ensemble
DTE = VotingClassifier(estimators=models,voting='soft')
return DTE
Rand_Forest = RandomForestClassifier(n_estimators=10,criterion='gini',max_depth=None)
def SVM_Ensemble():
# Develop SVM Ensemble
models = list()
models.append(('SVM1', SVC(probability=True, kernel='rbf', C=1.0,gamma=0.1)))
models.append(('SVM2', SVC(probability=True, kernel='poly', C = 0.01, degree=3, gamma=0.01)))
models.append(('SVM3', SVC(probability=True, kernel='sigmoid', C=0.5, gamma=0.001)))
models.append(('SVM4', SVC(probability=True, kernel='rbf', C=0.1,gamma=1.0)))
models.append(('SVM5', SVC(probability=True, kernel='poly', C = 0.25, degree=5, gamma=0.01)))
# define the voting ensemble
SVE = VotingClassifier(estimators=models, voting='soft')
return SVE
def MLP_Ensemble():
# Develop SVM Ensemble
models = list()
models.append(('MLP1', MLPClassifier(hidden_layer_sizes=(25,25,25),activation="relu",solver='adam',
learning_rate="adaptive",learning_rate_init=0.1, max_iter=1000)))
models.append(('MLP2', MLPClassifier(hidden_layer_sizes=(50,25,25),activation="relu",solver='sgd',
learning_rate="constant",learning_rate_init=0.001, max_iter=1000)))
models.append(('MLP3', MLPClassifier(hidden_layer_sizes=(50,25,50),activation="tanh",solver='lbfgs',
learning_rate="adaptive",learning_rate_init=0.0001, max_iter=1000)))
models.append(('MLP4', MLPClassifier(hidden_layer_sizes=(50,50,50),activation="logistic",solver='sgd',
learning_rate="constant",learning_rate_init=0.01, max_iter=1000)))
models.append(('MLP5', MLPClassifier(hidden_layer_sizes=(50,50,25),activation="tanh",solver='adam',
learning_rate="adaptive",learning_rate_init=0.00001, max_iter=1000)))
# define the voting ensemble
MLPE = VotingClassifier(estimators=models, voting='soft')
return MLPE
print('\n')
# Developing heterogeneous ensemble
def get_HTRGN_ensemble():
models = list()
models.append(('NB_ensemble', NB_Ensemble()))
models.append(('kNN_ensemble', kNN_Ensemble()))
models.append(('DT_ensemble', DT_Ensemble()))
models.append(('RF', Rand_Forest))
models.append(('SVM_ensemble', SVM_Ensemble()))
models.append(('MLP_ensemble', MLP_Ensemble()))
HTE = VotingClassifier(estimators=models,voting='soft')
return HTE
# Get a list of models to evaluate
def get_models():
models = dict()
models['NB_HE'] = NB_Ensemble()
models['kNN_HE'] = kNN_Ensemble()
models['DT_HE'] = DT_Ensemble()
models['RF'] = Rand_Forest
models['SVM_HE'] = SVM_Ensemble()
models['ANN_HE'] = MLP_Ensemble()
models['HTE'] = get_HTRGN_ensemble()
return models
# evaluate a given model using cross-validation
def evaluate_model(model, X_train, y_train):
scores = cross_val_score(model, X_train, y_train, scoring='accuracy', cv=cv_method, n_jobs=-1)
return scores
# get the models to evaluate
models = get_models()
# evaluate the models and store results
results, names = list(), list()
print('Cross Validation Mean Accuracy and Std Dev of each Ensemble on test set:----------------------------------')
for name, model in models.items():
scores = evaluate_model(model, X_test, y_test)
results.append(scores)
names.append(name)
print('>%s %.3f' % (name, mean(scores)),u"\u00B1", '%.3f' % std(scores))
# plot model performance for comparison
plt.boxplot(results, labels=names, showfliers=False)
#plt.title('Cross validation Accuracy of ensembles')
#plt.title("Sonar_without_bag_{}".format(bagsize))
plt.xlabel("Ensembles")
plt.ylabel("Accuracy of Ensembles")
#plt.show()
plt.savefig('Nursery_Hyperparameter_Output')
print('\n')
print('Cross Validation Mean Accuracy and Std Dev of each Ensemble on train set:-----------------------------')
for name, model in models.items():
# evaluate the model
scores = evaluate_model(model, X_train, y_train)
# store the results
results.append(scores)
names.append(name)
# summarize the performance along the way
print('>%s %.3f' % (name, mean(scores)), u"\u00B1", '%.3f' % std(scores))
print('\n')
model_probab = list()
expert_prediction = list()
# Train and evaluate each Ensemble
for name,model in models.items():
# fit the model
model.fit(X_train,y_train)
# then predict on the test set
y_pred= model.predict(X_test)
expert_prediction.append(y_pred)
# Evaluate the models
print('Performance Results of', name, ':----------------------------------------------------------')
test_acc = accuracy_score(y_test,y_pred)
y_pred1= model.predict(X_train)
train_acc = accuracy_score(y_train,y_pred1)
# Computing Generalizaton Factor
test_err = 1-test_acc # generalization error
train_err = 1-train_acc # training error
gen_factor = test_err/train_err
print('Accuracy and test error of', name, 'on test set:', test_acc,u"\u00B1",test_err)
print('Actual label:',y_test)
print('Predicted label:',y_pred)
print('Accuracy and training error of', name, 'on train set:', train_acc,u"\u00B1",train_err)
print('Generalization Factor to determine Ensemble Overfitting',gen_factor)
# NOTE: if the gen_factor > 1, then the ensemble overfits else it is desirable
# Classification Report: This gives us how often the algorithm predicted correctly
clf_report= classification_report(y_test,y_pred)
# Confusion Matrix: Showing the correctness and misclassifications made my the models
conf = confusion_matrix(y_test, y_pred)
print('Classification Report for', name,':')
print(clf_report)
print()
print('Confusion Matrix for',name, ':')
print(conf)
print('\n')
# Compute the probabilities of each ensemble to get ROC_AUC scores
probs = model.predict_proba(X_test)
model_probab.append(probs)
# Evaluate Bias-Variance Tradeoff
avg_expected_loss2, avg_bias2, avg_variance2 = bias_variance_decomp(model, X_train, y_train
, X_test, y_test, loss='0-1_loss',
num_rounds=10,
random_seed=20)
# Summary of Results
print('Average Expected loss for', name, '%.2f' % avg_expected_loss2)
print('Average Expected Bias error for', name, '%.2f' % avg_bias2)
print('Average Expected Variance error for', name, '%.2f' % avg_variance2)
print('\n')
# Classification Report: This gives us how often the algorithm predicted correctly
def class_report(y_true, y_pred, y_score=None, average='micro'):
if y_true.shape != y_pred.shape:
print("Error! y_true %s is not the same shape as y_pred %s" % (
y_true.shape,
y_pred.shape)
)
return
lb = LabelBinarizer()
if len(y_true.shape) == 1:
lb.fit(y_true)
# Value counts of predictions
labels, cnt = np.unique(
y_pred,
return_counts=True)
n_classes = len(labels)
pred_cnt = pd.Series(cnt, index=labels)
metrics_summary = precision_recall_fscore_support(
y_true=y_true,
y_pred=y_pred,
labels=labels)
avg = list(precision_recall_fscore_support(
y_true=y_true,
y_pred=y_pred,
average='weighted'))
metrics_sum_index = ['precision', 'recall', 'f1-score', 'support']
class_report_df = pd.DataFrame(
list(metrics_summary),
index=metrics_sum_index,
columns=labels)
support = class_report_df.loc['support']
total = support.sum()
class_report_df['avg / total'] = avg[:-1] + [total]
class_report_df = class_report_df.T
class_report_df['pred'] = pred_cnt
class_report_df['pred'].iloc[-1] = total
if not (y_score is None):
fpr = dict()
tpr = dict()
roc_auc = dict()
for label_it, label in enumerate(labels):
fpr[label], tpr[label], _ = roc_curve(
(y_true == label).astype(int),
y_score[:, label_it])
roc_auc[label] = auc(fpr[label], tpr[label])
if average == 'micro':
if n_classes <= 2:
fpr["avg / total"], tpr["avg / total"], _ = roc_curve(
lb.transform(y_true).ravel(),
y_score[:, 1].ravel())
else:
fpr["avg / total"], tpr["avg / total"], _ = roc_curve(
lb.transform(y_true).ravel(),
y_score.ravel())
roc_auc["avg / total"] = auc(
fpr["avg / total"],
tpr["avg / total"])
elif average == 'macro':
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([
fpr[i] for i in labels]
))
# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in labels:
mean_tpr += interp(all_fpr, fpr[i], tpr[i])
# Finally average it and compute AUC
mean_tpr /= n_classes
fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["avg / total"] = auc(fpr["macro"], tpr["macro"])
class_report_df['AUC'] = pd.Series(roc_auc)
return class_report_df
clf_report = class_report(y_test, y_pred, y_score=model.predict_proba(X_test))
print('Classification Report for', name, ':')
print(clf_report)
print('\n')
print('Gathering Predictions of Experts----------------------------------------------------------')
# Expert Prediction
NB_HE_pred = expert_prediction[0]
kNN_HE_pred = expert_prediction[1]
DT_HE_pred = expert_prediction[2]
RF_pred = expert_prediction[3]
SVM_HE_pred = expert_prediction[4]
MLP_HE_pred = expert_prediction[5]
HTE_pred = expert_prediction[6]
# put each expert's predictions into a dataframe
df1 = pd.DataFrame(NB_HE_pred,columns=['NB_HE'])
df2 = pd.DataFrame(kNN_HE_pred,columns=['kNN_HE'])
df3 = pd.DataFrame(DT_HE_pred,columns=['DT_HE'])
df4 = pd.DataFrame(RF_pred,columns=['RF'])
df5 = pd.DataFrame(SVM_HE_pred,columns=['SVM_HE'])
df6 = pd.DataFrame(MLP_HE_pred,columns=['MLP_HE'])
df7 = pd.DataFrame(HTE_pred,columns=['HTE'])
# Put the dataframes into a list
df = [df1,df2,df3,df4,df5,df6,df7]
# Concatenate the dataframes
gather_pred = pd.concat(df,axis=1)
print(gather_pred)