Kaggle competition: PadhAI: Text - Non Text Classification Level 4a
titanic=titanic.drop(['Name'],axis=1 )<count_values>
np.random.seed(100)
 LEVEL = 'level_4a'
titanic.Surname.value_counts()<groupby>
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 )
titanic['SurnameFreq']=titanic.groupby('Surname')['Surname'].transform('count')
<count_values>
languages = ['ta', 'hi', 'en']
images_train = read_all(".. /input/level_4a_train/"+LEVEL+"/"+"background", key_prefix='bgr_')
 for language in languages:
images_train.update(read_all(".. /input/level_4a_train//"+LEVEL+"/"+language, key_prefix=language+"_"))
print(len(images_train))
images_test = read_all(".. /input/level_4a_test/kaggle_"+LEVEL, key_prefix='')
 print(len(images_test))
titanic.Ticket.value_counts() 
<groupby>
X_train = []
Y_train = []
for key, value in images_train.items() :
X_train.append(value)
 if key[:4] == "bgr_":
Y_train.append(0)
 else:
Y_train.append(1)
 ID_test = []
X_test = []
for key, value in images_test.items() :
ID_test.append(int(key))
X_test.append(value)
 X_train = np.array(X_train)
 Y_train = np.array(Y_train)
 X_test = np.array(X_test)
 print(X_train.shape, Y_train.shape)
 print(X_test.shape )
titanic['TicketFreq']=titanic.groupby('Ticket')['Ticket'].transform('count')
<feature_engineering>
scaler = MinMaxScaler()
X_scaled_train = scaler.fit_transform(X_train)
 X_scaled_test = scaler.transform(X_test)
titanic['customizedFare']=titanic.Fare/(titanic.TicketFreq*titanic.Pclass )<count_missing_values>
sn_ce = SigmoidNeuron()
sn_ce.fit(X_scaled_train, Y_train, epochs=200, learning_rate=0.015, loss_fn="ce", display_loss=True )
titanic.isnull().sum()<feature_engineering>
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 )
titanic.loc[(titanic.Age.isnull())&(titanic.Title=='Master'),'Age']=int(4.0 )<feature_engineering>
print_accuracy(sn_ce )
titanic.loc[(titanic.Age.isnull())&(titanic.Sex=='male'),'Age']=int(30.0 )<feature_engineering>
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
submission.to_csv("submisision_6.csv", index=False)
<feature_engineering><EOS> 
submission.groupby('Class' ).count()