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2020-045
2020-045
Commits
2ed0d124
Commit
2ed0d124
authored
Apr 17, 2020
by
bjanadi
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Training Classifiers
parent
6c58a1df
Pipeline
#907
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3
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1
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Training_Data/Classifiers/SciKit.py
Training_Data/Classifiers/SciKit.py
+0
-74
Training_Data/Classifiers/SciKit2.py
Training_Data/Classifiers/SciKit2.py
+0
-71
Training_Data/Classifiers/iris.csv
Training_Data/Classifiers/iris.csv
+0
-151
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Training_Data/Classifiers/SciKit.py
deleted
100644 → 0
View file @
6c58a1df
import
pandas
as
pd
import
matplotlib.pyplot
as
plt
import
seaborn
as
sns
data
=
pd
.
read_csv
(
'iris.csv'
)
print
(
data
)
x
=
data
.
iloc
[:,:
-
1
]
.
values
y
=
data
.
iloc
[:,
-
1
]
.
values
print
(
x
)
print
(
y
)
from
sklearn.preprocessing
import
LabelEncoder
ly
=
LabelEncoder
()
y
=
ly
.
fit_transform
(
y
)
sns
.
set
()
sns
.
pairplot
(
data
[[
'sepal_length'
,
'sepal_width'
,
'petal_length'
,
'petal_width'
,
'species'
]],
hue
=
"species"
,
diag_kind
=
"kde"
)
from
sklearn.model_selection
import
train_test_split
x_train
,
x_test
,
y_train
,
y_test
=
train_test_split
(
x
,
y
,
test_size
=
0.2
)
from
sklearn.naive_bayes
import
GaussianNB
gnb
=
GaussianNB
()
gnb
.
fit
(
x_train
,
y_train
)
y_pred_test
=
gnb
.
predict
(
x_test
)
from
sklearn.metrics
import
accuracy_score
acc
=
accuracy_score
(
y_test
,
y_pred_test
)
from
sklearn.linear_model
import
LogisticRegression
logreg
=
LogisticRegression
(
solver
=
'lbfgs'
,
multi_class
=
'auto'
)
logreg
.
fit
(
x_train
,
y_train
)
y_pred
=
logreg
.
predict
(
x_test
)
from
sklearn.metrics
import
accuracy_score
acc1
=
accuracy_score
(
y_test
,
y_pred
)
from
sklearn.tree
import
DecisionTreeClassifier
dt
=
DecisionTreeClassifier
()
dt
.
fit
(
x_train
,
y_train
)
y_pred2
=
dt
.
predict
(
x_test
)
acc2
=
accuracy_score
(
y_test
,
y_pred2
)
from
sklearn.neighbors
import
KNeighborsClassifier
clf
=
KNeighborsClassifier
(
n_neighbors
=
3
,
algorithm
=
'ball_tree'
)
clf
.
fit
(
x_train
,
y_train
)
y_pred3
=
clf
.
predict
(
x_test
)
acc3
=
accuracy_score
(
y_test
,
y_pred3
)
from
sklearn.svm
import
SVC
svc1
=
SVC
(
C
=
50
,
kernel
=
'rbf'
,
gamma
=
1
)
svc1
.
fit
(
x_train
,
y_train
)
y_pred4
=
svc1
.
predict
(
x_test
)
from
sklearn.metrics
import
accuracy_score
acc4
=
accuracy_score
(
y_test
,
y_pred4
)
# print accuracy
print
(
"Accuracy scores"
)
print
(
"Decision Tree Classifier: "
,
acc2
)
print
(
"KNN Classifier: "
,
acc
)
print
(
"SVM Classifier: "
,
acc4
)
print
(
"Naive Bayes Classifier: "
,
acc
)
print
(
"Logistic Regression: "
,
acc1
)
Training_Data/Classifiers/SciKit2.py
deleted
100644 → 0
View file @
6c58a1df
# importing necessary libraries
from
sklearn
import
datasets
from
sklearn.metrics
import
confusion_matrix
from
sklearn.model_selection
import
train_test_split
# loading the iris dataset
iris
=
datasets
.
load_iris
()
# X -> features, y -> label
X
=
iris
.
data
y
=
iris
.
target
print
(
X
,
y
)
# dividing X, y into train and test data
X_train
,
X_test
,
y_train
,
y_test
=
train_test_split
(
X
,
y
,
random_state
=
0
)
# training a DescisionTreeClassifier
from
sklearn.tree
import
DecisionTreeClassifier
dtree_model
=
DecisionTreeClassifier
(
max_depth
=
2
)
.
fit
(
X_train
,
y_train
)
dtree_predictions
=
dtree_model
.
predict
(
X_test
)
#accuracy
DecisionAccuracy
=
dtree_model
.
score
(
X_test
,
y_test
)
# creating a confusion matrix
cm
=
confusion_matrix
(
y_test
,
dtree_predictions
)
# training a linear SVM classifier
from
sklearn.svm
import
SVC
svm_model_linear
=
SVC
(
kernel
=
'linear'
,
C
=
1
)
.
fit
(
X_train
,
y_train
)
svm_predictions
=
svm_model_linear
.
predict
(
X_test
)
# model accuracy for X_test
SVMaccuracy
=
svm_model_linear
.
score
(
X_test
,
y_test
)
# creating a confusion matrix
cm
=
confusion_matrix
(
y_test
,
svm_predictions
)
# training a KNN classifier
from
sklearn.neighbors
import
KNeighborsClassifier
knn
=
KNeighborsClassifier
(
n_neighbors
=
7
)
.
fit
(
X_train
,
y_train
)
# accuracy on X_test
KNNaccuracy
=
knn
.
score
(
X_test
,
y_test
)
# creating a confusion matrix
knn_predictions
=
knn
.
predict
(
X_test
)
cm
=
confusion_matrix
(
y_test
,
knn_predictions
)
# training a Naive Bayes classifier
from
sklearn.naive_bayes
import
GaussianNB
gnb
=
GaussianNB
()
.
fit
(
X_train
,
y_train
)
gnb_predictions
=
gnb
.
predict
(
X_test
)
# accuracy on X_test
Bayesaccuracy
=
gnb
.
score
(
X_test
,
y_test
)
# creating a confusion matrix
cm
=
confusion_matrix
(
y_test
,
gnb_predictions
)
print
(
"Accuracy scores"
)
print
(
"Decision Tree Classifier: "
,
DecisionAccuracy
)
print
(
"KNN Classifier: "
,
KNNaccuracy
)
print
(
"SVM Classifier: "
,
SVMaccuracy
)
print
(
"Naive Bayes Classifier: "
,
Bayesaccuracy
)
Training_Data/Classifiers/iris.csv
deleted
100644 → 0
View file @
6c58a1df
sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3,1.4,0.1,setosa
4.3,3,1.1,0.1,setosa
5.8,4,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5,3,1.6,0.2,setosa
5,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.1,1.5,0.1,setosa
4.4,3,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5,3.3,1.4,0.2,setosa
7,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5,2,3.5,1,versicolor
5.9,3,4.2,1.5,versicolor
6,2.2,4,1,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3,4.5,1.5,versicolor
5.8,2.7,4.1,1,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3,5,1.7,versicolor
6,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1,versicolor
5.8,2.7,3.9,1.2,versicolor
6,2.7,5.1,1.6,versicolor
5.4,3,4.5,1.5,versicolor
6,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3,4.1,1.3,versicolor
5.5,2.5,4,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3,4.6,1.4,versicolor
5.8,2.6,4,1.2,versicolor
5,2.3,3.3,1,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3,5.8,2.2,virginica
7.6,3,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3,5.5,2.1,virginica
5.7,2.5,5,2,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6,2.2,5,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2,virginica
7.7,2.8,6.7,2,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6,3,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3,5.2,2.3,virginica
6.3,2.5,5,1.9,virginica
6.5,3,5.2,2,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3,5.1,1.8,virginica
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