Kaggle competition: PadhAI: Text - Non Text Classification Level 4b
X_test = test_data.drop(['PassengerId', 'Survived', 'Name', 'SibSp', 'Parch', 'Ticket'],axis=1)
 y_test = test_data[target]<prepare_x_and_y>
class SigmoidNeuron:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
 def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
 def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
 else:
raise ValueError("y should be 0 or 1")
 def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False):
if initialise:
self.w = np.random.randn(1, X.shape[1])
 self.b = 0
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
 db += self.grad_b_mse(x, y)
 elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
 db += self.grad_b_ce(x, y)
 self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
 elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
 if display_loss:
plt.plot(loss.values())
 plt.xlabel('Epochs')
 if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
 elif loss_fn == "ce":
plt.ylabel('Log Loss')
 plt.show()
def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
 return np.array(Y_pred )
X_val = validation_data.drop(['PassengerId', 'Survived', 'Name', 'SibSp', 'Parch', 'Ticket'],axis=1)
 y_val = validation_data[target]<categorify>
languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4b_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
 for language in languages:
images_train.update(read_all(".. /input/level_4b_train/"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4b_test/kaggle_"+LEVEL, key_prefix='')
 print(len(images_test))
y_enc = LabelEncoder()
y_train = y_enc.fit_transform(y_train)
 y_test = y_enc.transform(y_test)
 y_val = y_enc.transform(y_val )<normalization>
scaler = StandardScaler()
X_scaled_train = scaler.fit_transform(X_train)
 X_scaled_test = scaler.transform(X_test )
sc = StandardScaler()
X_train[num_cols] = sc.fit_transform(X_train[num_cols])
 X_test[num_cols] = sc.transform(X_test[num_cols])
 X_val[num_cols] = sc.transform(X_val[num_cols] )<choose_model_class>
class SigmoidNeuronMy:
def __init__(self):
self.w = None
self.b = None
def perceptron(self, x):
return np.dot(x, self.w.T)+ self.b
def sigmoid(self, x):
return 1.0/(1.0 + np.exp(-x))
def grad_w_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)* x
def grad_b_mse(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
return(y_pred - y)* y_pred *(1 - y_pred)
 def grad_w_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred * x
elif y == 1:
return -1 *(1 - y_pred)* x
else:
raise ValueError("y should be 0 or 1")
 def grad_b_ce(self, x, y):
y_pred = self.sigmoid(self.perceptron(x))
if y == 0:
return y_pred
elif y == 1:
return -1 *(1 - y_pred)
 else:
raise ValueError("y should be 0 or 1")
 def fit(self, X, Y, epochs=1, learning_rate=1, initialise=True, loss_fn="mse", display_loss=False, setting = False):
if initialise:
self.w = np.random.randn(1, X.shape[1])
 self.b = 0
loss = {}
if display_loss:
loss = {}
for i in tqdm_notebook(range(epochs), total=epochs, unit="epoch"):
dw = 0
db = 0
for x, y in zip(X, Y):
if loss_fn == "mse":
dw += self.grad_w_mse(x, y)
 db += self.grad_b_mse(x, y)
 elif loss_fn == "ce":
dw += self.grad_w_ce(x, y)
 db += self.grad_b_ce(x, y)
 self.w -= learning_rate * dw
self.b -= learning_rate * db
if display_loss:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
 elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
 if setting:
Y_pred = self.sigmoid(self.perceptron(X))
if loss_fn == "mse":
loss[i] = mean_squared_error(Y, Y_pred)
 elif loss_fn == "ce":
loss[i] = log_loss(Y, Y_pred)
 if setting:
return list(loss.values())
 if display_loss:
plt.plot(loss.values())
 plt.xlabel('Epochs')
 if loss_fn == "mse":
plt.ylabel('Mean Squared Error')
 elif loss_fn == "ce":
plt.ylabel('Log Loss')
 plt.show()
def optimumValue(self , X , Y, loss_fn="mse"):
lr = np.linspace(0.000005, 0.00003, 6)
 epochs = np.array([500])
 acc = {}
min_loss = 1000
lr_fin = 0
epoch_fin = 0
std = 0
fin = []
for j in tqdm_notebook(range(len(lr)) , total=len(lr), unit="lr"):
for i in tqdm_notebook(range(len(epochs)) , total=len(epochs), unit="epoch"):
for k in range(4):
X1_train, X1_test, y1_train, y1_test = train_test_split(X, Y, test_size=0.2, random_state=k**2)
 self.fit(X1_train, y1_train, epochs=epochs[i].astype("int")+1, learning_rate=lr[j], loss_fn="ce", display_loss=True)
 Y_pred_train = self.predict(X1_test)
 Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
acc[k] = accuracy_score(Y_pred_binarised_train, y1_test)
 std=statistics.stdev(list(acc.values()))
accuracy = max(list(acc.values()))
fin.append([accuracy,std,epochs[i].astype("int")+1,lr[j]])
 print(fin)
 def predict(self, X):
Y_pred = []
for x in X:
y_pred = self.sigmoid(self.perceptron(x))
Y_pred.append(y_pred)
 return np.array(Y_pred)
clf = LogisticRegression()<train_model>
clf.fit(X_train,y_train )<predict_on_test>
sn_ce = SigmoidNeuronMy()
sn_ce.fit(X_scaled_train, Y_train, epochs=800, learning_rate=0.00005, loss_fn="ce", display_loss=True)
y_pred = clf.predict(X_test )<compute_test_metric>
def print_accuracy(sn):
Y_pred_train = sn.predict(X_scaled_train)
 Y_pred_binarised_train =(Y_pred_train >= 0.5 ).astype("int" ).ravel()
accuracy_train = accuracy_score(Y_pred_binarised_train, Y_train)
 print("Train Accuracy : ", accuracy_train)
 print("-"*50 )
confusion_matrix(y_test,y_pred )<compute_test_metric>
print_accuracy(sn_ce )
<define_variables><EOS> 
Y_pred_test = sn_ce.predict(X_scaled_test)
 Y_pred_binarised_test =(Y_pred_test >= 0.5 ).astype("int" ).ravel()
submission = {}
submission['ImageId'] = ID_test
submission['Class'] = Y_pred_binarised_test
submission = pd.DataFrame(submission)
 submission = submission[['ImageId', 'Class']]
submission = submission.sort_values(['ImageId'])
 submission.to_csv("submisision.csv", index=False )