
Python Assignment Help on A to J one Prototype
- 20th May, 2022
- 16:41 PM
import pandas as pd import numpy as np import pandas as pd import numpy as np import random alp=['a','b','c','d','e','f','g','h','i','j'] d={} ls=[] for k in range(0,10): matrix=[] ls=[] for i in range(7): row=[] for j in range(7): row.append(random.randint(0,1)) matrix.append(row) d[alp[k]]=np.squeeze(np.asarray(matrix)) d s=pd.DataFrame(columns=['0','1','2','3','4','5','6','label'],index=list(range(70))) i=0 for j in range(len(d)): for m in range(7): for k in range(7): s.iloc[i,k]=(d[list(d.keys())[j]][m][k]) s.iloc[i,k+1]=list(d.keys())[j] i=i+1 # i=i+7 x=s[['0','1','2','3','4','5','6']] y=s['label'] # Random Forest from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3) clf = RandomForestClassifier(max_depth=50, random_state=0) clf.fit(X_train,y_train) y_pred=clf.predict(X_test) from sklearn import metrics # Model Accuracy, how often is the classifier correct? print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) #SVM Classifier from sklearn.svm import SVC svclassifier = SVC(kernel='linear') svclassifier.fit(X_train, y_train) y_pred = svclassifier.predict(X_test) from sklearn.metrics import classification_report, confusion_matrix print(confusion_matrix(y_test,y_pred)) print(classification_report(y_test,y_pred)) from sklearn import metrics # Model Accuracy, how often is the classifier correct? print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) #MLP Classifier from sklearn.neural_network import MLPClassifier #Initializing the MLPClassifier classifier = MLPClassifier(hidden_layer_sizes=(150,100,50), max_iter=300,activation = 'relu',solver='adam',random_state=1) #Fitting the training data to the network classifier.fit(X_train, y_train) #Predicting y for X_val y_pred = classifier.predict(X_test) from sklearn import metrics # Model Accuracy, how often is the classifier correct? print("Accuracy:",metrics.accuracy_score(y_test, y_pred))