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Machine Learning Algos.py
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Machine Learning Algos.py
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'''7 MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION TASK - 3 CLASSES - IRIS DATASET'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris()
x = iris.data[:, 2:][0:140]
y = iris.target[0:140]
x_test = iris.data[:, 2:][141:150]
y_test = iris.target[141:150]
x[np.argsort(x[:, 1])]
y[np.argsort(y)]
'''NAIVE BAYES'''
from sklearn.naive_bayes import GaussianNB
model1=GaussianNB()
model1.fit(x,y)
nb=model1.score(x,y)
pred=model1.predict(x)
t1=sum(x==0 for x in pred-y)/len(pred)
'''DECISION TREES'''
from sklearn import tree
model2=tree.DecisionTreeClassifier(
class_weight= None,
criterion= 'entropy',
max_depth= 20,
max_features= x.shape[1],
max_leaf_nodes= 4,
min_samples_leaf= 1,
min_samples_split= 1,
min_weight_fraction_leaf= 0.0,
presort= False,
random_state= None,
splitter= 'best')
model2.fit(x,y)
dt=model2.score(x,y)
pred=model2.predict(x_test)
t2=sum(x==0 for x in pred-y_test)/len(pred)
'''BOOSTING'''
from sklearn.ensemble import GradientBoostingClassifier
model3=GradientBoostingClassifier(
init= None,
learning_rate= 0.6,
loss= 'deviance',
max_depth= 5,
max_features= x.shape[1],
max_leaf_nodes= 4,
min_samples_leaf= 1,
min_samples_split= 1,
min_weight_fraction_leaf= 0.0,
n_estimators= 1000,
presort= 'auto',
random_state= None,
subsample= 1.0,
verbose=1,
warm_start= False)
model3.fit(x,y)
boo=model3.score(x,y)
pred=model3.predict(x_test)
t3=sum(x==0 for x in pred-y_test)/len(pred)
'''K NEAREST NEIGHBOR'''
from sklearn.neighbors import KNeighborsClassifier
model4=KNeighborsClassifier(
n_neighbors=3,
algorithm= 'auto',
leaf_size= 30,
metric= 'minkowski',
metric_params= None,
n_jobs= 1,
p= 2,
weights= 'uniform')
model4.fit(x,y)
knn=model4.score(x,y)
pred=model4.predict(x_test)
t4=sum(x==0 for x in pred-y_test)/len(pred)
'''MULTINOMIAL LOGISTIC REGRESSION'''
from sklearn.linear_model import LogisticRegression
model5=LogisticRegression(
C= 1.0,
class_weight= 'balanced',
dual= False,
fit_intercept= True,
intercept_scaling= 1,
max_iter= 50000,
multi_class= 'multinomial',
n_jobs= 2,
penalty= 'l2',
random_state= None,
solver= 'newton-cg',
tol= 0.0001,
verbose= 1,
warm_start= False)
model5.fit(x,y)
log=model5.score(x,y)
pred=model5.predict(x_test)
t5=sum(x==0 for x in pred-y_test)/len(pred)
'''SUPPORT VECTOR MACHINES'''
from sklearn import svm
model6=svm.SVC(
C= 1.0,
cache_size= 10,
class_weight= None,
coef0= 0.0,
decision_function_shape= None,
degree= 3,
gamma= 100,
kernel= 'rbf',
max_iter= -1,
probability= True,
random_state= None,
shrinking= True,
tol= 0.001,
verbose=1)
model6.fit(x,y)
svm_=model6.score(x,y)
pred=model6.predict(x_test)
t6=sum(x==0 for x in pred-y_test)/len(pred)
'''RANDOM FORESTS'''
from sklearn.ensemble import RandomForestClassifier
model7=RandomForestClassifier(
bootstrap= True,
class_weight= None,
criterion= 'gini',
max_depth= 100,
max_features=x.shape[1],
max_leaf_nodes=100,
min_samples_leaf= 1,
min_samples_split= 1,
min_weight_fraction_leaf= 0,
n_estimators= 100,
n_jobs= 1,
oob_score= False,
random_state= None,
verbose= 1,
warm_start=True)
model7.fit(x,y)
rand=model7.score(x,y)
pred=model7.predict(x_test)
t7=sum(x==0 for x in pred-y_test)/len(pred)
ss2=[nb,dt,boo,knn,log,svm_,rand]
ss2