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# Machine Learning Sample |
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Some machine learnings example for my activity on freeday XD |
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Built in with Scikit-learn library |
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# 6 Algorithms Used on This Project |
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1. KNN |
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2. Logistic Linear |
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3. Decission Tree Classifier |
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4. SVM |
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5. Gaussian NB |
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6. Linear Discrimination Analysis |
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# 2 Evaluate Models For The Datasets |
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1. K Fold CV ( K Fold Cross Validation ) |
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2. LOOCV ( Leave One Out Cross Validation ) |
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# How it choose it's own best algorithm |
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There are 2 evaluate models in this project. Each models produce an array that filled up with 6 models and it's accuracy score. So it'll look for the best one and pick it up as the algorithm to use. |
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# Next Step? |
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1. Glass Classification Section |
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2. Image Classification with default datasets from scikit-learn itself |
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3. Maybe fixed the method of choosing the best algorithm, because sometimes there are 2 algorithms that had same score. So it may use both of them and show it's result. |
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# Author |
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Robby Muhammad Nst |
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OrionStark |
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164,1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,5 |
||||
165,1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,5 |
||||
166,1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,5 |
||||
167,1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,5 |
||||
168,1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,5 |
||||
169,1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,5 |
||||
170,1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,5 |
||||
171,1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,5 |
||||
172,1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,5 |
||||
173,1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,5 |
||||
174,1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,5 |
||||
175,1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,5 |
||||
176,1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,5 |
||||
177,1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,6 |
||||
178,1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,6 |
||||
179,1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,6 |
||||
180,1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6 |
||||
181,1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6 |
||||
182,1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6 |
||||
183,1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6 |
||||
184,1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6 |
||||
185,1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6 |
||||
186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7 |
||||
187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7 |
||||
188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7 |
||||
189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7 |
||||
190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7 |
||||
191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7 |
||||
192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7 |
||||
193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7 |
||||
194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7 |
||||
195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7 |
||||
196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7 |
||||
197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7 |
||||
198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7 |
||||
199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7 |
||||
200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7 |
||||
201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7 |
||||
202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7 |
||||
203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7 |
||||
204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7 |
||||
205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7 |
||||
206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7 |
||||
207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7 |
||||
208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7 |
||||
209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7 |
||||
210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7 |
||||
211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7 |
||||
212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7 |
||||
213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7 |
||||
214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7 |
||||
@ -0,0 +1,94 @@ |
||||
1. Title: Glass Identification Database |
||||
|
||||
2. Sources: |
||||
(a) Creator: B. German |
||||
-- Central Research Establishment |
||||
Home Office Forensic Science Service |
||||
Aldermaston, Reading, Berkshire RG7 4PN |
||||
(b) Donor: Vina Spiehler, Ph.D., DABFT |
||||
Diagnostic Products Corporation |
||||
(213) 776-0180 (ext 3014) |
||||
(c) Date: September, 1987 |
||||
|
||||
3. Past Usage: |
||||
-- Rule Induction in Forensic Science |
||||
-- Ian W. Evett and Ernest J. Spiehler |
||||
-- Central Research Establishment |
||||
Home Office Forensic Science Service |
||||
Aldermaston, Reading, Berkshire RG7 4PN |
||||
-- Unknown technical note number (sorry, not listed here) |
||||
-- General Results: nearest neighbor held its own with respect to the |
||||
rule-based system |
||||
|
||||
4. Relevant Information:n |
||||
Vina conducted a comparison test of her rule-based system, BEAGLE, the |
||||
nearest-neighbor algorithm, and discriminant analysis. BEAGLE is |
||||
a product available through VRS Consulting, Inc.; 4676 Admiralty Way, |
||||
Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. |
||||
In determining whether the glass was a type of "float" glass or not, |
||||
the following results were obtained (# incorrect answers): |
||||
|
||||
Type of Sample Beagle NN DA |
||||
Windows that were float processed (87) 10 12 21 |
||||
Windows that were not: (76) 19 16 22 |
||||
|
||||
The study of classification of types of glass was motivated by |
||||
criminological investigation. At the scene of the crime, the glass left |
||||
can be used as evidence...if it is correctly identified! |
||||
|
||||
5. Number of Instances: 214 |
||||
|
||||
6. Number of Attributes: 10 (including an Id#) plus the class attribute |
||||
-- all attributes are continuously valued |
||||
|
||||
7. Attribute Information: |
||||
1. Id number: 1 to 214 |
||||
2. RI: refractive index |
||||
3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as |
||||
are attributes 4-10) |
||||
4. Mg: Magnesium |
||||
5. Al: Aluminum |
||||
6. Si: Silicon |
||||
7. K: Potassium |
||||
8. Ca: Calcium |
||||
9. Ba: Barium |
||||
10. Fe: Iron |
||||
11. Type of glass: (class attribute) |
||||
-- 1 building_windows_float_processed |
||||
-- 2 building_windows_non_float_processed |
||||
-- 3 vehicle_windows_float_processed |
||||
-- 4 vehicle_windows_non_float_processed (none in this database) |
||||
-- 5 containers |
||||
-- 6 tableware |
||||
-- 7 headlamps |
||||
|
||||
8. Missing Attribute Values: None |
||||
|
||||
Summary Statistics: |
||||
Attribute: Min Max Mean SD Correlation with class |
||||
2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 |
||||
3. Na: 10.73 17.38 13.4079 0.8166 0.5030 |
||||
4. Mg: 0 4.49 2.6845 1.4424 -0.7447 |
||||
5. Al: 0.29 3.5 1.4449 0.4993 0.5988 |
||||
6. Si: 69.81 75.41 72.6509 0.7745 0.1515 |
||||
7. K: 0 6.21 0.4971 0.6522 -0.0100 |
||||
8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 |
||||
9. Ba: 0 3.15 0.1750 0.4972 0.5751 |
||||
10. Fe: 0 0.51 0.0570 0.0974 -0.1879 |
||||
|
||||
9. Class Distribution: (out of 214 total instances) |
||||
-- 163 Window glass (building windows and vehicle windows) |
||||
-- 87 float processed |
||||
-- 70 building windows |
||||
-- 17 vehicle windows |
||||
-- 76 non-float processed |
||||
-- 76 building windows |
||||
-- 0 vehicle windows |
||||
-- 51 Non-window glass |
||||
-- 13 containers |
||||
-- 9 tableware |
||||
-- 29 headlamps |
||||
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,50 @@ |
||||
# MIT License |
||||
|
||||
# Copyright (c) 2018 Robby Muhammad Nst |
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
# of this software and associated documentation files (the "Software"), to deal |
||||
# in the Software without restriction, including without limitation the rights |
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
# copies of the Software, and to permit persons to whom the Software is |
||||
# furnished to do so, subject to the following conditions: |
||||
|
||||
# The above copyright notice and this permission notice shall be included in all |
||||
# copies or substantial portions of the Software. |
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
# SOFTWARE. |
||||
|
||||
import pandas |
||||
import numpy |
||||
from pandas.plotting import scatter_matrix |
||||
import matplotlib.pyplot as plt |
||||
import sys |
||||
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 |
||||
sys.path.append('..') |
||||
from utils.models_evaluation import ModelsEvaluation |
||||
from utils.model import Model |
||||
|
||||
# Load the dataset |
||||
path = "../datasets/Glass Dataset/glass.data" |
||||
names = ['id', 'RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe', 'class'] |
||||
dataset = pandas.read_csv(path, names = names) |
||||
array = dataset.values |
||||
X = array[:, 1:9] |
||||
Y = array[:, 10] |
||||
print(X) |
||||
print(Y) |
||||
@ -0,0 +1,28 @@ |
||||
# MIT License |
||||
|
||||
# Copyright (c) 2018 Robby Muhammad Nst |
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
# of this software and associated documentation files (the "Software"), to deal |
||||
# in the Software without restriction, including without limitation the rights |
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
# copies of the Software, and to permit persons to whom the Software is |
||||
# furnished to do so, subject to the following conditions: |
||||
|
||||
# The above copyright notice and this permission notice shall be included in all |
||||
# copies or substantial portions of the Software. |
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
# SOFTWARE. |
||||
|
||||
from sklearn.datasets import load_digits |
||||
import matplotlib.pyplot as plt |
||||
digits = load_digits() |
||||
plt.gray() |
||||
plt.matshow(digits.images[0]) |
||||
plt.show() |
||||
@ -0,0 +1,54 @@ |
||||
import pandas |
||||
import numpy |
||||
from pandas.plotting import scatter_matrix |
||||
import matplotlib.pyplot as plt |
||||
import sys |
||||
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 |
||||
sys.path.append('..') |
||||
from utils.models_evaluation import ModelsEvaluation |
||||
from utils.model import Model |
||||
|
||||
# Load the dataset |
||||
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" |
||||
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class'] |
||||
dataset = pandas.read_csv(url, names=names) |
||||
|
||||
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) |
||||
|
||||
MODEL_EVALUATION = ModelsEvaluation(X, Y) |
||||
HIGHEST_ACC_MODEL = MODEL_EVALUATION.evaluateAccuracy().name |
||||
HIGHEST_ACC_MODEL_SCORE = MODEL_EVALUATION.evaluateAccuracy().mean |
||||
HIGHEST_MODEL_INSTANCE = Model.getAlgorithmModel(HIGHEST_ACC_MODEL) |
||||
HIGHEST_MODEL_INSTANCE.fit(X, Y) |
||||
|
||||
# Predict data included sepal-length(cm), sepal-width(cm), petal-length(cm), petal-width(cm) |
||||
predict_data = [ |
||||
[5.7, 2.8, 4.1, 1.3], |
||||
[5.8, 2.7, 5.1, 1.9], |
||||
[7.7, 3.0, 6.1, 2.3] |
||||
] |
||||
prediction = HIGHEST_MODEL_INSTANCE.predict(predict_data) |
||||
|
||||
if ( prediction is not None ): |
||||
print("Match Algorithm is " + HIGHEST_ACC_MODEL) |
||||
print("Confident : ", HIGHEST_ACC_MODEL_SCORE * 100) |
||||
print(prediction) |
||||
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False) |
||||
scatter_matrix(dataset) |
||||
plt.show() |
||||
else: |
||||
print("Couldn't find the match algorithm") |
||||
@ -0,0 +1,21 @@ |
||||
# MIT License |
||||
|
||||
# Copyright (c) 2018 Robby Muhammad Nst |
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
# of this software and associated documentation files (the "Software"), to deal |
||||
# in the Software without restriction, including without limitation the rights |
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
# copies of the Software, and to permit persons to whom the Software is |
||||
# furnished to do so, subject to the following conditions: |
||||
|
||||
# The above copyright notice and this permission notice shall be included in all |
||||
# copies or substantial portions of the Software. |
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
# SOFTWARE. |
||||
Binary file not shown.
@ -0,0 +1,63 @@ |
||||
# MIT License |
||||
|
||||
# Copyright (c) 2018 Robby Muhammad Nst |
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
# of this software and associated documentation files (the "Software"), to deal |
||||
# in the Software without restriction, including without limitation the rights |
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
# copies of the Software, and to permit persons to whom the Software is |
||||
# furnished to do so, subject to the following conditions: |
||||
|
||||
# The above copyright notice and this permission notice shall be included in all |
||||
# copies or substantial portions of the Software. |
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
# SOFTWARE. |
||||
|
||||
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 |
||||
|
||||
class Model: |
||||
models = [] |
||||
|
||||
def __init__(self, name, mean, std): |
||||
""" Model constructor """ |
||||
self.name = name |
||||
self.mean = mean |
||||
self.std = std |
||||
|
||||
@staticmethod |
||||
def getHighestScore(): |
||||
""" Get the highest score algorithm method """ |
||||
__score = [] |
||||
__results = [] |
||||
for model in Model.models: |
||||
__score.append(model.mean) |
||||
__score.sort() |
||||
for i in range(len(Model.models)): |
||||
if Model.models[i].mean == __score[len(__score) - 1]: |
||||
__results.append(Model.models[i]) |
||||
return __results |
||||
|
||||
@staticmethod |
||||
def getAlgorithmModel(name): |
||||
""" Generate the result from getHighestScore name attribute """ |
||||
return { |
||||
'Logistic Regression' : LogisticRegression(), |
||||
'Linear Discrimination Analysis' : LinearDiscriminantAnalysis(), |
||||
'DecissionTreeClassifier' : DecisionTreeClassifier(), |
||||
'SVM' : SVC(), |
||||
'Gaussian NB' : GaussianNB(), |
||||
'K NeighborsClassifier' : KNeighborsClassifier() |
||||
}.get(name, None) |
||||
|
||||
Binary file not shown.
@ -0,0 +1,101 @@ |
||||
# MIT License |
||||
|
||||
# Copyright (c) 2018 Robby Muhammad Nst |
||||
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy |
||||
# of this software and associated documentation files (the "Software"), to deal |
||||
# in the Software without restriction, including without limitation the rights |
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
||||
# copies of the Software, and to permit persons to whom the Software is |
||||
# furnished to do so, subject to the following conditions: |
||||
|
||||
# The above copyright notice and this permission notice shall be included in all |
||||
# copies or substantial portions of the Software. |
||||
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
||||
# SOFTWARE. |
||||
|
||||
import pandas |
||||
from pandas.plotting import scatter_matrix |
||||
import matplotlib.pyplot as plt |
||||
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 |
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis |
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from sklearn.naive_bayes import GaussianNB |
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from sklearn.svm import SVC |
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from model import Model |
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|
||||
class ModelsEvaluation: |
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__models = [] |
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def __init__(self, x_train, y_train): |
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|
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""" Models Evaluation Constructor |
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Initiallize 6 models """ |
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|
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self.x_train = x_train |
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self.y_train = y_train |
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|
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# 6 default algorith models |
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# Maybe I will add more algorithm in the future |
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self.__models.append(('Logistic Regression', LogisticRegression())) |
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self.__models.append(('Linear Discrimination Analysis', LinearDiscriminantAnalysis())) |
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self.__models.append(('DecissionTreeClassifier', DecisionTreeClassifier())) |
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self.__models.append(('SVM', SVC())) |
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self.__models.append(('Gaussian NB', GaussianNB())) |
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self.__models.append(('K NeighborsClassifier', KNeighborsClassifier())) |
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|
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# Evaluate the accuracy of 6 models |
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# K fold Validation model |
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def evaluateAccuracy(self): |
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Model.models[:] = [] |
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|
||||
""" Evalueate the accuracy model by given data train. |
||||
So it could get the best Algorithm to use """ |
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|
||||
__results = [] |
||||
__names = [] |
||||
for name, model in self.__models: |
||||
kfold = model_selection.KFold(n_splits = 10, random_state = 7) |
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cv_results = model_selection.cross_val_score(model, self.x_train, self.y_train, cv = kfold, scoring = 'accuracy') |
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__results.append(cv_results) |
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__names.append(name) |
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Model.models.append(Model(name, cv_results.mean(), cv_results.std())) |
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|
||||
# NOT IMPLEMENTED YET |
||||
# |
||||
# if ( len(Model.getHighestScore()) > 1 ): |
||||
# if (Model.getHighestScore()[0].mean == Model.getHighestScore()[1].mean): |
||||
# return Model.getHighestScore()[0] |
||||
# else: |
||||
# return Model.getHighestScore() |
||||
# else: |
||||
# return Model.getHighestScore()[0] |
||||
|
||||
return Model.getHighestScore()[0] |
||||
|
||||
# LOOCV Validation Model |
||||
def leaveOneOutCrossValidationEvaluation(self): |
||||
Model.models[:] = [] |
||||
|
||||
__results = [] |
||||
__names = [] |
||||
__looCrossValidation = model_selection.LeaveOneOut() |
||||
for name, model in self.__models: |
||||
cv_results = model_selection.cross_val_score(model, self.x_train, self.y_train, cv = __looCrossValidation) |
||||
__results.append(cv_results) |
||||
__names.append(name) |
||||
Model.models.append(Model(name, cv_results.mean(), cv_results.std())) |
||||
|
||||
return Model.getHighestScore()[0] |
||||
|
||||
|
||||
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