Create AlgorithmsClassification.py

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Nuño Sempere 2019-10-09 20:41:24 +02:00 committed by GitHub
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directory = '/home/nuno/Documents/Jobs/IDInsight'
import pandas as pd
import numpy as np
import joblib
## Install the dataframe
insuranceDataFrame = pd.read_csv('/home/nuno/Documents/Jobs/IDInsight/insurance_clean_continuous.csv')
insuranceDataFrame['charges']
## We divide our data into training and test sets, and normalize
dfTrain = insuranceDataFrame[:1000]
dfTest = insuranceDataFrame[1000:1300]
dfCheck = insuranceDataFrame[1300:]
means = np.mean(dfTrain, axis=0)
stds = np.std(dfTrain, axis=0)
dfTrain = (dfTrain - means) / stds
dfTest = (dfTest - means) / stds
dfCheck = (dfCheck - means) / stds
dfTrain['charges'] = (dfTrain['charges']>=0).astype('int')
dfTest['charges'] = (dfTest['charges']>=0).astype('int')
dfCheck['charges'] = (dfCheck['charges']>=0).astype('int')
## Convert our stuff to arrays
trainLabel = np.asarray(dfTrain['charges'])
trainData = np.asarray(dfTrain.drop('charges',1))
testLabel = np.asarray(dfTest['charges'])
testData = np.asarray(dfTest.drop('charges',1))
## ******************************************************************
## We're ready to apply the specific ML algorithms!
# Naïve Bayes
## Naïve Bayes: Bernoulli
from sklearn.naive_bayes import BernoulliNB
insuranceCheck = BernoulliNB(alpha=0.01)
insuranceCheck.fit(trainData, trainLabel)
score = insuranceCheck.score(testData, testLabel)
print("score = ", score * 100, "/100")
# We know that the error is R^2, we just find it more intuitive to present this as a score out of 100.
algorithms = []
algorithms = np.append(algorithms, input())
scores = []
scores = np.append(scores, score)
## We could use the following to make predictions
## But for the moment, we're just interested in seeing which algorithm performs best, so we'll only do this once.
sampleData = dfCheck[4:5]
sampleDataFeatures = np.asarray(sampleData.drop('charges',1))
prediction = insuranceCheck.predict(sampleDataFeatures)
print('Insurance Claim Prediction:', prediction)
sampleData['charges']
## Save the model
joblib.dump([insuranceCheck, means, stds], directory+'/insuranceModel-NaiveBayes-Bernoulli.pkl')
## This is what we would do to load the next time
insuranceLoadedModel, means, stds = joblib.load(directory+ '/insuranceModel-NaiveBayes-Bernoulli.pkl')
score = insuranceLoadedModel.score(testData, testLabel)
print("score = ", score * 100,"/ 100")
## ******************************************************************
## Naïve Bayes: Gaussian
from sklearn.naive_bayes import GaussianNB
insuranceCheck = GaussianNB()
insuranceCheck.fit(trainData, trainLabel)
score = insuranceCheck.score(testData, testLabel)
print("score = ", score * 100, "/100")
algorithms = np.append(algorithms, input())
scores = np.append(scores, score)
## Save the model
joblib.dump([insuranceCheck, means, stds], directory+'/insuranceModel-NaiveBayes-Gaussian.pkl')
## ******************************************************************
## Nearest Neighbours
### We've been using the same setup for a while; we should create a function!
def runAlgorithm(Classifier, algorithmName):
global scores, algorithms
Classifier.fit(trainData, trainLabel)
scoreInternal = Classifier.score(testData, testLabel)
print("score = ", scoreInternal * 100, "/100")
scores = np.append(scores, scoreInternal) ## These are global scope variables
algorithms = np.append(algorithms, algorithmName)
joblib.dump([Classifier, means, stds],
'/home/nuno/Documents/Jobs/IDInsight/insuranceModel-' + algorithmName + '.pkl')
algorithmName = "NearestNeighbours"
from sklearn.neighbors import KNeighborsClassifier
insuranceCheck = KNeighborsClassifier(n_jobs=-1, n_neighbors=7, weights="distance", algorithm="brute", leaf_size=10, p=2)
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
## Support Vector Machines
from sklearn.svm import SVC
algorithmName = "SVM"
insuranceCheck = SVC(gamma='scale', kernel='poly', degree=5) # Required some tinkering
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
## Decision Trees
from sklearn.tree import DecisionTreeClassifier
algorithmName = "Tree"
### This is getting a little bit repetitive, so let's do some rudimentary hyperparameter optimization
score = 0
## Warning: slow
for criterion in np.array(["gini", "entropy"]):
for splitter in np.array(["best", "random"]):
for max_depth in np.append(range(1,10), [None]):
print("max_depth=",max_depth)
for min_samples_split in (np.asarray(range(25))+2):
#print("min_samples_split=", min_samples_split)
for min_samples_leaf in (np.asarray(range(25))+1):
for max_features in np.array(["log2", "auto", None]):
insuranceCheck = DecisionTreeClassifier(random_state=0, criterion=criterion, splitter = splitter, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, max_features = max_features)
insuranceCheck.fit(trainData, trainLabel)
score_temp = insuranceCheck.score(testData, testLabel)
if(score_temp > score):
print("score = ", score_temp * 100, "/100")
print("\nNEW BEST\ncriterion=", criterion, "\nsplitter =", splitter, "\nmax_depth =", max_depth, "\nmin_samples_split =", min_samples_split, "\nmin_samples_leaf =", min_samples_leaf, "\nmax_features =", max_features)
score = score_temp
insuranceCheck = DecisionTreeClassifier(random_state=0, criterion="gini", splitter = "best", max_depth = 9, min_samples_split = 15, min_samples_leaf = 4, max_features = "log2")
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
## Random forests
from sklearn.ensemble import RandomForestClassifier
algorithmName = "RandomForest"
insuranceCheck = RandomForestClassifier(n_estimators=200, criterion = "gini", min_samples_split = 14, min_samples_leaf=4, max_depth=9 , random_state=0, n_jobs=-1, max_features=None)
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
## Extra random trees
algorithmName = "ExtraRandomTrees"
from sklearn.ensemble import ExtraTreesClassifier
insuranceCheck = ExtraTreesClassifier(n_estimators=300, max_depth=None)
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
## MultiLayerPerceptron (NN)
algorithmName = "MultiLayerPerceptron"
from sklearn.neural_network import MLPClassifier
insuranceCheck = MLPClassifier(max_iter=800, hidden_layer_sizes=150)
runAlgorithm(insuranceCheck, algorithmName)
## ******************************************************************
for i in range(len(scores)):
print(algorithms[i], ": ", scores[i])
## And that's it. We notice that most of our algorithms do pretty well, with a score of .89 - .93, where the maximum is 1.
## This means that a lot of the variability in the sample is extracted by our model.