Init Commit

6 years ago · 4536ebc6cf
parent ae89e08979
commit 4536ebc6cf
14 changed files with 652 additions and 0 deletions
--- a/README.md
+++ b/README.md
@ -0,0 +1,27 @@
+# Machine Learning Sample
+Some machine learnings example for my activity on freeday XD
+Built in with Scikit-learn library
+
+# 6 Algorithms Used on This Project
+1. KNN
+2. Logistic Linear
+3. Decission Tree Classifier
+4. SVM
+5. Gaussian NB
+6. Linear Discrimination Analysis
+
+# 2 Evaluate Models For The Datasets
+1. K Fold CV ( K Fold Cross Validation )
+2. LOOCV ( Leave One Out Cross Validation )
+
+# How it choose it's own best algorithm
+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.
+
+# Next Step?
+1. Glass Classification Section
+2. Image Classification with default datasets from scikit-learn itself
+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.
+
+# Author
+Robby Muhammad Nst  
+OrionStark
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+++ b/Dataset/glass.data
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+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
--- a/Dataset/glass.names
+++ b/Dataset/glass.names
@ -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
+
+
+
+
--- a/glass-classification/README.md
+++ b/glass-classification/README.md
--- a/glass-classification/index.py
+++ b/glass-classification/index.py
@ -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)
--- a/image-classification/index.py
+++ b/image-classification/index.py
@ -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()
--- a/iris-classification/README.md
+++ b/iris-classification/README.md
--- a/iris-classification/index.py
+++ b/iris-classification/index.py
@ -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")
--- a/utils/init.py
+++ b/utils/init.py
@ -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.
--- a/utils/init.pyc
+++ b/utils/init.pyc
--- a/utils/model.py
+++ b/utils/model.py
@ -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)
+
--- a/utils/model.pyc
+++ b/utils/model.pyc
--- a/utils/models_evaluation.py
+++ b/utils/models_evaluation.py
@ -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
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+from model import Model
+
+class ModelsEvaluation:
+    __models = []
+    def __init__(self, x_train, y_train):
+
+        """ Models Evaluation Constructor
+        Initiallize 6 models """
+
+        self.x_train = x_train
+        self.y_train = y_train
+
+        # 6 default algorith models
+        # Maybe I will add more algorithm in the future
+        self.__models.append(('Logistic Regression', LogisticRegression()))
+        self.__models.append(('Linear Discrimination Analysis', LinearDiscriminantAnalysis()))
+        self.__models.append(('DecissionTreeClassifier', DecisionTreeClassifier()))
+        self.__models.append(('SVM', SVC()))
+        self.__models.append(('Gaussian NB', GaussianNB()))
+        self.__models.append(('K NeighborsClassifier', KNeighborsClassifier()))
+
+    # Evaluate the accuracy of 6 models
+    # K fold Validation model
+    def evaluateAccuracy(self):
+        Model.models[:] = []
+
+        """ Evalueate the accuracy model by given data train. 
+            So it could get the best Algorithm to use """
+
+        __results = []
+        __names = []
+        for name, model in self.__models:
+            kfold = model_selection.KFold(n_splits = 10, random_state = 7)
+            cv_results = model_selection.cross_val_score(model, self.x_train, self.y_train, cv = kfold, scoring = 'accuracy')
+            __results.append(cv_results)
+            __names.append(name)
+            Model.models.append(Model(name, cv_results.mean(), cv_results.std()))
+
+        # 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]
+
+    
--- a/utils/models_evaluation.pyc
+++ b/utils/models_evaluation.pyc