First attempt on vehicle dataset with a random forest classifier

dev/Sidrah-Madiha/allcustommodules.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn import metrics
+from math import sqrt
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sn
+from sklearn.metrics import confusion_matrix, classification_report
+
+
+
+def data_stats(dataset):
+    ''' Shows some basic stats of the dataset'''
+    print("=========== SOME STATS of Dataset ===========")
+    print('Shape of the dataset: ' + str(dataset.shape) + "\n")
+    print('List of attribute columns' , list(dataset.columns))
+    print("\n")
+    list_cat = dataset.Class.unique()
+    print('List of Categories ' , list_cat , "\n" )
+
+
+def tokenize_target_column(dataset):
+    ''' tokenize the Class column values to numeric data'''
+    factor = pd.factorize(dataset['Class'])
+    dataset.Class = factor[0]
+    definitions = factor[1]
+    print("Updated tokenize 'Class' column - first 5 values")
+    print(dataset.Class.head())
+    print("Distinct Tokens used for converting Class column to integers")
+    print(definitions)
+    return definitions
+def train_data_test_data_split(dataset):
+    X = dataset.iloc[:, 0:-1].values
+    y = dataset.iloc[:,-1].values
+#     print(X[0])
+#     print(y[0])
+#     print(X.shape)
+#     print(y.shape)
+#     print('the data attributes columns')
+#     print(X[:5,:])
+#     print('The target variable: ')
+#     print(y[:5])
+#     Split dataset into training set and test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 ,random_state = 21)
+    return X_train, X_test, y_train, y_test
+
+def train(X_train, y_train):
+    ''' training model on train data'''
+    classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 42)
+    classifier.fit(X_train, y_train)
+    return classifier
+
+def test(classifier, X_test):
+    ''' testing model on test data'''
+    y_pred=classifier.predict(X_test)
+    return y_pred
+
+def untokenizing_testdata_prediction(y_test, y_pred, definitions):
+    '''Converting numeric target and predict values back to original labels'''
+    reversefactor = dict(zip(range(4),definitions))
+    y_test = np.vectorize(reversefactor.get)(y_test)
+    y_pred = np.vectorize(reversefactor.get)(y_pred)
+    return y_test, y_pred
+
+
+def create_confusion_matrix_class_report(y_test, y_pred):
+    ''' Creates Cinfusion Matrix and summary of evaluation metric '''
+
+    labels = ["van" , "saab" ,"bus" , "opel"]
+    cm = confusion_matrix(y_test, y_pred, labels=labels)
+    df_cm = pd.DataFrame(cm, index=labels, columns=labels)
+
+    sn.heatmap(df_cm, annot=True, fmt='d')
+    plt.xlabel('Real Vehicle Category')
+    plt.ylabel('Predicted Vehicle Category')
+    print("============== Summary of all evaluation metics ===============")
+    print(classification_report(y_test,y_pred))
+    print ("====================== Confusion Matrix=====================")
+
+