Source: View original notebook on GitHub
Category: Machine Learning / Learn ML
Challenge - Chemicals Segregation
Chemical Segregation(Classification) A chemist has two chemical flasks labelled 1 and 0 which contains two different chemicals. He extracted 3 features from these chemicals in order to distinguish between them. You are provided with the results derived by the chemist and your task is to create a model that will label chemical 0 or 1 given its three features.
Data Description You are provided with two files test and train.
Train: This files consists of two csv files LogisticXtrain and LogisticYtrain. Xtrain consists of the features whereas Ytrain consists of the labels associated with the features.
Test: This file consists of two files LogisticXtest consisting of the features of test data and sample_output which represents in which format your solution csv must be submitted.
You need to implement any classifier from scratch, don't use any sklearn based classifier.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Classifier from Scratch
# returning hx for each sample hence, a numpy array of size (m X 1)
def hypothesis(X,theta):
return np.sum(X*theta[1:], axis=1).reshape((-1,1)) + theta[0] # using numpy broadcasting
# retuning sigmoid for each sample hence, a numpy array of size (m X 1)
def sigmoid(X,theta):
hx = hypothesis(X,theta)
return 1.0 / (1.0 + np.exp(-1*hx))
# returning scalar value as error
def negative_log_likelihood(X,Y,theta):
g_h_x = sigmoid(X,theta)
log_liklihood = np.sum(Y * np.log2(g_h_x) + (1-Y) * np.log2(1 - g_h_x) )
return -1 * log_liklihood
# return gradients w.r.t theta ,size = (n+1,)
def gradient(X,Y,theta):
grad = np.zeros(X.shape[1] + 1)
hx = sigmoid(X,theta)
grad[0] = np.sum(hx - Y)
for i in range(1, X.shape[1] + 1):
mul = X[:,i-1].reshape((-1,1))
grad[i] = np.sum((hx - Y)*mul)
return grad
# goal of this function is to minimize the ``Negative of log of likelihood`` using graident descent
# code is similar to Linear Regression but hypothesis function is different
def classifier(X,Y,learning_rate=0.0001):
# Y should have a shape with atleast two axis, otherwise use reshape((-1,1))
theta = np.zeros(X.shape[1] + 1)
error = []
err = negative_log_likelihood(X,Y,theta)
error.append(err)
while True:
# print(error[-1])
grad = gradient(X,Y,theta)
theta = theta - learning_rate * grad
err = negative_log_likelihood(X,Y,theta)
if abs(err - error[-1]) < 0.0001:
break
error.append(err)
return theta
def predict(X_test, theta):
import numpy as np
g_h_x = sigmoid(X_test,theta)
Y_pred = []
for i in range(X_test.shape[0]):
if g_h_x[i] >= 0.5:
Y_pred.append(1)
else:
Y_pred.append(0)
return np.array(Y_pred)
def accuracy(Y_actual, Y_predict):
total = Y_actual.shape[0]
diff = np.sum(Y_actual == Y_predict)
return diff / total
# loading data
X = pd.read_csv('Datasets/Assignment3_Logistic_X_Train.csv')
Y = pd.read_csv('Datasets/Assignment3_Logistic_Y_Train.csv')
test = pd.read_csv('Datasets/Assignment3_Logistic_X_Test.csv')
X.head()
Output:
f1 f2 f3
0 -1.239375 0.749101 -0.528515
1 -1.036070 0.801436 -1.283712
2 -0.615579 1.579521 -1.391927
3 1.335978 1.348651 1.433564
4 0.658925 1.300019 0.571603
X.shape
Output:
(3000, 3)
Y.shape
Output:
(3000, 1)
test.shape
Output:
(1000, 3)
# preprocessing
from sklearn.preprocessing import StandardScaler
s = StandardScaler()
s.fit_transform(X)
s.transform(test)
Output:
array([[-0.58096727, -1.23196981, -2.10386172],
[-0.93642077, 0.43931113, -0.36596031],
[ 1.43773502, 0.91090225, 1.08581064],
...,
[-0.89542577, -0.05073748, -0.67463414],
[-0.40465638, 0.8263143 , -0.84593545],
[ 0.39940977, -0.40861529, 0.29533219]])
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state=11)
theta = classifier(X_train.values,Y_train.values)
theta
Output:
array([-4.65946819, -4.20626944, 3.45533749, -2.4243403 ])
Y_pred = predict(X_test.values, theta)
Y_pred.shape
Output:
(750,)
acc = accuracy(Y_test,Y_pred.reshape((-1,1)))
acc
Output:
label 0.996
dtype: float64
# output file
output = predict(test.values,theta)
output.dtype
Output:
dtype('int32')
output.shape
Output:
(1000,)
df = pd.DataFrame(output)
df.columns = ['label']
df.head()
Output:
label
0 1
1 1
2 0
3 1
4 1
df.to_csv('Datasets/Assignment3_Logistic_scratch_output.csv',index=False)
df = pd.read_csv('Datasets/Assignment3_Logistic_scratch_output.csv')
df
Output:
label
0 1
1 1
2 0
3 1
4 1
5 1
6 0
7 1
8 0
9 0
10 0
11 1
12 1
13 1
14 1
15 0
16 1
17 0
18 1
19 0
20 1
21 0
22 1
23 0
24 1
25 1
26 0
27 0
28 0
29 1
.. ...
970 1
971 1
972 0
973 1
974 0
975 0
976 0
977 1
978 0
979 0
980 1
981 0
982 1
983 1
984 0
985 1
986 1
987 0
988 1
989 1
990 0
991 0
992 0
993 1
994 1
995 0
996 0
997 1
998 1
999 0
[1000 rows x 1 columns]
using SKlearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# loading data
X = pd.read_csv('Datasets/Assignment3_Logistic_X_Train.csv')
Y = pd.read_csv('Datasets/Assignment3_Logistic_Y_Train.csv')
test = pd.read_csv('Datasets/Assignment3_Logistic_X_Test.csv')
# preprocessing
from sklearn.preprocessing import StandardScaler
s = StandardScaler()
s.fit_transform(X)
s.transform(test)
Output:
array([[-0.58096727, -1.23196981, -2.10386172],
[-0.93642077, 0.43931113, -0.36596031],
[ 1.43773502, 0.91090225, 1.08581064],
...,
[-0.89542577, -0.05073748, -0.67463414],
[-0.40465638, 0.8263143 , -0.84593545],
[ 0.39940977, -0.40861529, 0.29533219]])
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25)
Y_train = np.ravel(Y_train)
Y_train.shape
Output:
(2250,)
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(solver='lbfgs')
model.fit(X_train,Y_train)
Output:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='warn',
n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',
tol=0.0001, verbose=0, warm_start=False)
model.coef_
Output:
array([[-3.74054432, 3.00706952, -2.20505995]])
model.intercept_
Output:
array([-3.94248248])
Y_test = np.ravel(Y_test)
Y_test.shape
Output:
(750,)
Y_pred = model.predict(X_test)
Y_pred.shape
Output:
(750,)
output = model.predict(test)
output.shape
Output:
(1000,)
output
Output:
array([1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,
0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1,
1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0,
1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1,
1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0,
1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,
1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1,
1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1,
0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,
0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1,
0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,
0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1,
1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1,
1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,
1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,
0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1,
1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,
... (output truncated)
model.score(X_test,Y_test)
Output:
0.9946666666666667
df = pd.DataFrame(output)
df.columns = ['label']
df.to_csv('Datasets/Assignment3_Logistic_output.csv',index=False)
df = pd.read_csv('Datasets/Assignment3_Logistic_output.csv')
df
Output:
label
0 1
1 1
2 0
3 1
4 1
5 1
6 0
7 1
8 0
9 0
10 0
11 1
12 1
13 1
14 1
15 0
16 1
17 0
18 1
19 0
20 1
21 0
22 1
23 0
24 1
25 1
26 0
27 0
28 0
29 1
.. ...
970 1
971 1
972 0
973 1
974 0
975 0
976 0
977 1
978 0
979 0
980 1
981 0
982 1
983 1
984 0
985 1
986 1
987 0
988 1
989 1
990 0
991 0
992 0
993 1
994 1
995 0
996 0
997 1
998 1
999 0
[1000 rows x 1 columns]