Add competition files

competition/multi-class_example/MCclassifier.py

@@ -0,0 +1,22 @@
+
+# 1. Importing new CSV data in pandas dataframes
+import pandas as pd
+data = pd.read_csv("iris_extension.csv")
+
+# 2. Separating labels from data
+y = data["label"]
+data = data.drop(columns=["label"])
+x = data.to_numpy()
+
+# 3. Loading a trained model
+import pickle
+model = pickle.load(open('iris_classif_model.sav', 'rb'))
+y_pred = model.predict(x)
+
+# 4. Evaluating the model with the new data
+from sklearn.metrics import classification_report, confusion_matrix
+print("\n *************** MODEL EVALUATION ****************")
+print("Confusion matrix:")
+print(confusion_matrix(y, y_pred))
+print(classification_report(y,y_pred)) 
+

competition/multi-class_example/MCmodel_trainer.py

@@ -0,0 +1,36 @@
+
+# 1. Importing CSV data for training in pandas dataframes
+import pandas as pd
+data = pd.read_csv("iris_base.csv")
+
+# 2. Separating labels from data
+y = data["label"]
+data = data.drop(columns=["label"])
+x = data.to_numpy()
+
+# 3. Splitting data into training/test subsets for model training and validation
+from sklearn.model_selection import train_test_split
+x_train, x_test, y_train, y_test = train_test_split(data, y, test_size=0.2, stratify=y)
+
+# 4. Fitting a Naive Gaussian classifier with the training split
+from sklearn.naive_bayes import GaussianNB
+gnb = GaussianNB()
+gnb.fit(x_train,y_train)
+
+# 5. The obtained model is tested with both the training and test split
+# to ensure no underfitting and overfitting issues
+y_pred_train = gnb.predict(x_train)
+y_pred_test = gnb.predict(x_test)
+from sklearn.metrics import classification_report, confusion_matrix
+print("\n *************** TRAINING ****************")
+print("\n Confusion matrix:")
+print(confusion_matrix(y_train, y_pred_train))
+print(classification_report(y_train,y_pred_train)) 
+print("\n ************** VALIDATION ***************")
+print("\n Confusion matrix:")
+print(confusion_matrix(y_test, y_pred_test))
+print(classification_report(y_test,y_pred_test)) 
+
+# 6. Saving the obtained model
+import pickle
+pickle.dump(gnb, open('iris_classif_model.sav', 'wb'))

competition/multi-class_example/iris_base.csv

@@ -0,0 +1,101 @@
+,sepal.length,sepal.width,petal.length,petal.width,label
+58,6.6,2.9,4.6,1.3,Versicolor
+62,6.0,2.2,4.0,1.0,Versicolor
+98,5.1,2.5,3.0,1.1,Versicolor
+137,6.4,3.1,5.5,1.8,Virginica
+54,6.5,2.8,4.6,1.5,Versicolor
+83,6.0,2.7,5.1,1.6,Versicolor
+56,6.3,3.3,4.7,1.6,Versicolor
+119,6.0,2.2,5.0,1.5,Virginica
+66,5.6,3.0,4.5,1.5,Versicolor
+103,6.3,2.9,5.6,1.8,Virginica
+148,6.2,3.4,5.4,2.3,Virginica
+105,7.6,3.0,6.6,2.1,Virginica
+45,4.8,3.0,1.4,0.3,Setosa
+134,6.1,2.6,5.6,1.4,Virginica
+47,4.6,3.2,1.4,0.2,Setosa
+136,6.3,3.4,5.6,2.4,Virginica
+92,5.8,2.6,4.0,1.2,Versicolor
+111,6.4,2.7,5.3,1.9,Virginica
+36,5.5,3.5,1.3,0.2,Setosa
+21,5.1,3.7,1.5,0.4,Setosa
+122,7.7,2.8,6.7,2.0,Virginica
+42,4.4,3.2,1.3,0.2,Setosa
+53,5.5,2.3,4.0,1.3,Versicolor
+115,6.4,3.2,5.3,2.3,Virginica
+17,5.1,3.5,1.4,0.3,Setosa
+123,6.3,2.7,4.9,1.8,Virginica
+132,6.4,2.8,5.6,2.2,Virginica
+29,4.7,3.2,1.6,0.2,Setosa
+141,6.9,3.1,5.1,2.3,Virginica
+142,5.8,2.7,5.1,1.9,Virginica
+40,5.0,3.5,1.3,0.3,Setosa
+69,5.6,2.5,3.9,1.1,Versicolor
+118,7.7,2.6,6.9,2.3,Virginica
+7,5.0,3.4,1.5,0.2,Setosa
+102,7.1,3.0,5.9,2.1,Virginica
+39,5.1,3.4,1.5,0.2,Setosa
+4,5.0,3.6,1.4,0.2,Setosa
+5,5.4,3.9,1.7,0.4,Setosa
+96,5.7,2.9,4.2,1.3,Versicolor
+147,6.5,3.0,5.2,2.0,Virginica
+91,6.1,3.0,4.6,1.4,Versicolor
+26,5.0,3.4,1.6,0.4,Setosa
+120,6.9,3.2,5.7,2.3,Virginica
+3,4.6,3.1,1.5,0.2,Setosa
+129,7.2,3.0,5.8,1.6,Virginica
+73,6.1,2.8,4.7,1.2,Versicolor
+48,5.3,3.7,1.5,0.2,Setosa
+72,6.3,2.5,4.9,1.5,Versicolor
+140,6.7,3.1,5.6,2.4,Virginica
+107,7.3,2.9,6.3,1.8,Virginica
+114,5.8,2.8,5.1,2.4,Virginica
+27,5.2,3.5,1.5,0.2,Setosa
+33,5.5,4.2,1.4,0.2,Setosa
+88,5.6,3.0,4.1,1.3,Versicolor
+95,5.7,3.0,4.2,1.2,Versicolor
+28,5.2,3.4,1.4,0.2,Setosa
+43,5.0,3.5,1.6,0.6,Setosa
+37,4.9,3.6,1.4,0.1,Setosa
+50,7.0,3.2,4.7,1.4,Versicolor
+97,6.2,2.9,4.3,1.3,Versicolor
+80,5.5,2.4,3.8,1.1,Versicolor
+22,4.6,3.6,1.0,0.2,Setosa
+9,4.9,3.1,1.5,0.1,Setosa
+86,6.7,3.1,4.7,1.5,Versicolor
+8,4.4,2.9,1.4,0.2,Setosa
+146,6.3,2.5,5.0,1.9,Virginica
+63,6.1,2.9,4.7,1.4,Versicolor
+76,6.8,2.8,4.8,1.4,Versicolor
+121,5.6,2.8,4.9,2.0,Virginica
+38,4.4,3.0,1.3,0.2,Setosa
+41,4.5,2.3,1.3,0.3,Setosa
+16,5.4,3.9,1.3,0.4,Setosa
+10,5.4,3.7,1.5,0.2,Setosa
+99,5.7,2.8,4.1,1.3,Versicolor
+85,6.0,3.4,4.5,1.6,Versicolor
+110,6.5,3.2,5.1,2.0,Virginica
+70,5.9,3.2,4.8,1.8,Versicolor
+93,5.0,2.3,3.3,1.0,Versicolor
+65,6.7,3.1,4.4,1.4,Versicolor
+49,5.0,3.3,1.4,0.2,Setosa
+108,6.7,2.5,5.8,1.8,Virginica
+133,6.3,2.8,5.1,1.5,Virginica
+12,4.8,3.0,1.4,0.1,Setosa
+139,6.9,3.1,5.4,2.1,Virginica
+143,6.8,3.2,5.9,2.3,Virginica
+35,5.0,3.2,1.2,0.2,Setosa
+138,6.0,3.0,4.8,1.8,Virginica
+71,6.1,2.8,4.0,1.3,Versicolor
+59,5.2,2.7,3.9,1.4,Versicolor
+60,5.0,2.0,3.5,1.0,Versicolor
+14,5.8,4.0,1.2,0.2,Setosa
+44,5.1,3.8,1.9,0.4,Setosa
+109,7.2,3.6,6.1,2.5,Virginica
+117,7.7,3.8,6.7,2.2,Virginica
+144,6.7,3.3,5.7,2.5,Virginica
+112,6.8,3.0,5.5,2.1,Virginica
+15,5.7,4.4,1.5,0.4,Setosa
+79,5.7,2.6,3.5,1.0,Versicolor
+55,5.7,2.8,4.5,1.3,Versicolor
+94,5.6,2.7,4.2,1.3,Versicolor

competition/multi-class_example/iris_extension.csv

@@ -0,0 +1,51 @@
+,sepal.length,sepal.width,petal.length,petal.width,label
+82,5.8,2.7,3.9,1.2,Versicolor
+31,5.4,3.4,1.5,0.4,Setosa
+90,5.5,2.6,4.4,1.2,Versicolor
+74,6.4,2.9,4.3,1.3,Versicolor
+1,4.9,3.0,1.4,0.2,Setosa
+81,5.5,2.4,3.7,1.0,Versicolor
+20,5.4,3.4,1.7,0.2,Setosa
+0,5.1,3.5,1.4,0.2,Setosa
+101,5.8,2.7,5.1,1.9,Virginica
+135,7.7,3.0,6.1,2.3,Virginica
+19,5.1,3.8,1.5,0.3,Setosa
+46,5.1,3.8,1.6,0.2,Setosa
+23,5.1,3.3,1.7,0.5,Setosa
+61,5.9,3.0,4.2,1.5,Versicolor
+87,6.3,2.3,4.4,1.3,Versicolor
+104,6.5,3.0,5.8,2.2,Virginica
+51,6.4,3.2,4.5,1.5,Versicolor
+11,4.8,3.4,1.6,0.2,Setosa
+34,4.9,3.1,1.5,0.2,Setosa
+127,6.1,3.0,4.9,1.8,Virginica
+52,6.9,3.1,4.9,1.5,Versicolor
+30,4.8,3.1,1.6,0.2,Setosa
+57,4.9,2.4,3.3,1.0,Versicolor
+75,6.6,3.0,4.4,1.4,Versicolor
+149,5.9,3.0,5.1,1.8,Virginica
+25,5.0,3.0,1.6,0.2,Setosa
+78,6.0,2.9,4.5,1.5,Versicolor
+131,7.9,3.8,6.4,2.0,Virginica
+116,6.5,3.0,5.5,1.8,Virginica
+89,5.5,2.5,4.0,1.3,Versicolor
+126,6.2,2.8,4.8,1.8,Virginica
+2,4.7,3.2,1.3,0.2,Setosa
+113,5.7,2.5,5.0,2.0,Virginica
+67,5.8,2.7,4.1,1.0,Versicolor
+145,6.7,3.0,5.2,2.3,Virginica
+64,5.6,2.9,3.6,1.3,Versicolor
+68,6.2,2.2,4.5,1.5,Versicolor
+13,4.3,3.0,1.1,0.1,Setosa
+130,7.4,2.8,6.1,1.9,Virginica
+18,5.7,3.8,1.7,0.3,Setosa
+128,6.4,2.8,5.6,2.1,Virginica
+124,6.7,3.3,5.7,2.1,Virginica
+6,4.6,3.4,1.4,0.3,Setosa
+24,4.8,3.4,1.9,0.2,Setosa
+84,5.4,3.0,4.5,1.5,Versicolor
+77,6.7,3.0,5.0,1.7,Versicolor
+32,5.2,4.1,1.5,0.1,Setosa
+125,7.2,3.2,6.0,1.8,Virginica
+100,6.3,3.3,6.0,2.5,Virginica
+106,4.9,2.5,4.5,1.7,Virginica