-
Notifications
You must be signed in to change notification settings - Fork 0
/
Predictive_Analytics.py
612 lines (537 loc) · 20.3 KB
/
Predictive_Analytics.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
# -*- coding: utf-8 -*-
"""
Predicitve_Analytics.py
"""
#using minmax normalization
import pandas as pd
import numpy as np
import time
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
import copy
import random
import matplotlib
dataframe = pd.read_csv('C:/Users/Documents/DIC/Assignment1/data.csv')
data_X = dataframe.iloc[:,0:48]
data_X = (data_X - np.min(data_X))/(np.max(data_X) - np.min(data_X)).values
data_X = np.array(data_X)
#print(data_X.shape)
data_Y = dataframe['48'].values
data_Y = np.array(data_Y)
#print(data_Y.shape)
#normalizing data
#from sklearn import preprocessing
#data_X = preprocessing.MinMaxScaler().fit_transform(data_X)
X_train, X_test, Y_train, Y_test = train_test_split(data_X, data_Y, test_size=0.3, random_state=50)
print("After splitting into train-test")
print("x train : ", X_train.shape)
print("x test : ", X_test.shape)
print("y train : ", Y_train.shape)
print("y test : ", Y_test.shape)
print(type(Y_test))
def Accuracy(y_true,y_pred):
"""
:type y_true: numpy.ndarray
:type y_pred: numpy.ndarray
:rtype: float
"""
accuracy_scores = []
correct_pred = 0
for i in range(len(y_true)):
if (y_true[i] == y_pred[i]):
correct_pred = correct_pred + 1
acc = (correct_pred / float(len(y_true))) * 100.0
accuracy_scores.append(acc)
accuracy = sum(accuracy_scores) / len(accuracy_scores)
return accuracy
def Recall(y_true,y_pred):
"""
:type y_true: numpy.ndarray
:type y_pred: numpy.ndarray
:rtype: float
"""
recall_scores = []
true_positive_cnt = 0
cnt = 0
for i in range(len(y_true)):
if(y_true[i] == 1):
cnt = cnt + 1
if(y_true[i] == y_pred[i]):
true_positive_cnt = true_positive_cnt + 1
recall_partial = (true_positive_cnt / cnt) * 100.0
recall_scores.append(recall_partial)
recall = sum(recall_scores) / len(recall_scores)
return recall
def Precision(y_true,y_pred):
"""
:type y_true: numpy.ndarray
:type y_pred: numpy.ndarray
:rtype: float
"""
precision_scores = []
true_positive_cnt = 0
cnt = 0
for i in range(len(y_true)):
if(y_pred[i] == 1):
cnt = cnt + 1
if(y_true[i] == y_pred[i]):
true_positive_cnt = true_positive_cnt + 1
precision_partial = (true_positive_cnt / cnt) * 100.0
precision_scores.append(precision_partial)
precision = sum(precision_scores) / len(precision_scores)
return precision
def WCSS(Clusters,centriods_use):
"""
:Clusters List[numpy.ndarray]
:rtype: float
"""
wcss = 0
# Euclidean Distance Caculator
def dist(a, b, ax=1):
return np.linalg.norm(a - b)
for i in range(len(Clusters)):
wcss = wcss + dist(Clusters[i],centriods_use[i]) * dist(Clusters[i],centriods_use[i])
#print(wcss)
return wcss
def ConfusionMatrix(y_true,y_pred):
"""
:type y_true: numpy.ndarray
:type y_pred: numpy.ndarray
:rtype: float
"""
classes = np.unique(np.concatenate((y_true,y_pred)))
conf_mat = np.empty((len(classes),len(classes)),dtype=np.int)
for i,x in enumerate(classes):
for j,y in enumerate(classes):
conf_mat[i,j] = np.where((y_true==x) * (y_pred==y))[0].shape[0]
return conf_mat
def KNN(X_train,X_test,Y_train,N):
"""
:type X_train: numpy.ndarray
:type X_test: numpy.ndarray
:type Y_train: numpy.ndarray
:rtype: numpy.ndarray
"""
#y_train_new=[]
#for i in Y_train:
#for j in i:
#y_train_new.append(j)
K=N
def distmat(a, b):
return np.linalg.norm(a - b,axis=1)
m=X_train.shape[0]
n=X_test.shape[0]
p=Y_train.shape[0]
q=Y_test.shape[0]
final_pred=[]
for i in range(n):
arr=np.tile(X_test[i],(m,1))
value=distmat(arr,X_train)
sorted_indices=np.argsort(value)
label_pred=[]
for q in range(K):
l=sorted_indices[q]
label_pred.append(Y_train[l])
a=np.array(label_pred)
counts = np.bincount(a)
final_pred.append(np.argmax(counts))
true_pred=np.array(final_pred)
return true_pred
def RandomForest(X_train,Y_train,X_test):
"""
:type X_train: numpy.ndarray
:type X_test: numpy.ndarray
:type Y_train: numpy.ndarray
:rtype: numpy.ndarray
"""
import sys
sys.setrecursionlimit(10**6)
n_trees=5
n_bootstrap=100
n_feat=2
y_train = Y_train.reshape(Y_train.shape[0], 1)
X_train_data=np.concatenate((X_train,y_train),axis=1)
#X_train_data=X_train_data[0:50]
#def random_forest(X_train_data,n_trees,n_bootstrap,n_feat):
forest=[]
def get_col_based_splits(X_train_data,col_split,val_split):
split_col_vals=X_train_data[:,col_split]
greater_vals=X_train_data[split_col_vals > val_split]
smaller_vals=X_train_data[split_col_vals <= val_split]
return smaller_vals,greater_vals
def compute_entropy(X_train_data):
label=X_train_data[:,-1]
_,cnt=np.unique(label,return_counts=True)
probabilities=cnt/cnt.sum()
entropy=sum(probabilities*-np.log2(probabilities)) #elementwise probability
return entropy
def bootstrapping(X_train_data,n_bootstrap):
bootstrap_indices = np.random.randint(low=0, high=len(X_train_data), size=n_bootstrap)
df_bootstrapped = X_train_data[bootstrap_indices]
return df_bootstrapped
def decision_tree(X_train_data,flag=0,min_samples=2,max_depth=5):
label=X_train_data[:,-1]
a=np.unique(label)
unique_classes, Uniq_counts=np.unique(label,return_counts=True)
largest_idx=Uniq_counts.argmax()
classification=unique_classes[largest_idx]
if len(a)==1:
return True
else:
flag+=1
n_splits={}
_,cols=X_train_data.shape
for i in range (cols-1):
n_splits[i]=[]
vals=np.unique(X_train_data[:,i])
for j in range(len(vals)):
if j!=0:
curr_val=vals[j]
prev_val=vals[j-1]
split=(curr_val+prev_val)/2
n_splits[i].append(split)
overall_entropy=999
for i in n_splits:
for val in n_splits[i]:
smaller_vals,greater_vals=get_col_based_splits(X_train_data,col_split=i,val_split=val)
all_data=len(smaller_vals)+len(greater_vals)
smaller_vals_pts=len(smaller_vals)/all_data
greater_vals_pts=len(greater_vals)/all_data
curr_total_entropy=(smaller_vals_pts*compute_entropy(smaller_vals)+greater_vals_pts*compute_entropy(greater_vals))
if curr_total_entropy<=overall_entropy:
overall_entropy=curr_total_entropy
best_split_col=i
best_split_val=val
smaller_vals,greater_vals=get_col_based_splits(X_train_data,best_split_col,best_split_val)
quest="{} <= {}".format(best_split_col,best_split_val)
subtree={quest:[]}
ans_y=decision_tree(smaller_vals,flag,min_samples,max_depth)
ans_n=decision_tree(greater_vals,flag,min_samples,max_depth)
subtree[quest].append(ans_y)
subtree[quest].append(ans_n)
return subtree
for x in range (n_trees):
df_bootstrap=bootstrapping(X_train_data,n_bootstrap)
tree=decision_tree(df_bootstrap)
forest.append(tree)
forest=np.array(forest)
return forest
def PCA(X_train,N):
"""
:type X_train: numpy.ndarray
:type N: int
:rtype: numpy.ndarray
"""
#Standardising the dataset by centering the mean and scaling each component to unit variance
X_train = preprocessing.scale(X_train)
#Computing the covariance matrix to find correlation between datapoints
X_covariance_matrix = np.cov(X_train.T)
#Finding eigen values, eigen vectors
eig_vals,eig_vects = np.linalg.eig(X_covariance_matrix)
#Forming eigenvalues,eigen-vector pairs
eig_pairs = [((eig_vals[i]),eig_vects[:,i])for i in range(len(eig_vals))]
#Sorting the eigenvalues
eig_pairs.sort(key=lambda X:X[0],reverse=True)
#Getting the top n_components vectors
n_comp_vects = eig_vects[:,:N]
#Finding the reduced dimensionality by multiplying it with original matrix
red_dim_mat = np.dot(X_train,n_comp_vects)
#print(red_dim_mat)
return red_dim_mat
def Kmeans(X_train,N):
"""
:type X_train: numpy.ndarray
:type N: int
:rtype: List[numpy.ndarray]
"""
K = N
m = X_train.shape[0]
n = X_train.shape[1]
# Euclidean Distance Calculator
def dist(a, b, ax=1):
return np.linalg.norm(a - b,)
#Taking Centroid matrix
Centroids=np.array([]).reshape(n,0)
#randomly selecting centroid
for i in range(K):
rand = random.randint(0,m-1)
Centroids = np.c_[Centroids,X_train[rand]]
#Taking transpose
centriods_use = Centroids.T
#Storing the value of centroids when it updates
centriods_previous = np.zeros(centriods_use.shape)
# Calculating Error func. - Distance between new centroids and old centroids
error = dist(centriods_use, centriods_previous, None)
array_index = []
centroid = []
for i in range(len(X_train)):
array_index.append(0)
centroid.append(0)
while error != 0:
# Assigning each value to its closest cluster
for i in range(len(X_train)):
length_dist = []
for j in range(K):
distances = dist(X_train[i], centriods_use[j])
length_dist.append(distances)
array_index[i] = length_dist.index(min(length_dist))
centroid[i] = centriods_use[array_index[i]]
centriods_previous = copy.deepcopy(centriods_use)
# Finding the new centroids by taking the average value
for i in range(K):
points = [X_train[j] for j in range(len(X_train)) if array_index[j] == i]
centriods_use[i] = np.mean(points, axis=0)
error = dist(centriods_use, centriods_previous, None)
sum1=0
clusters=[]
for i in range(K):
mini_clusters = []
for j in range(len(X_train)):
if(array_index[j] == i):
mini_clusters.append(X_train[j])
clusters.append(mini_clusters)
clusters1 = np.array(clusters)
return clusters1,centriods_use
y_pred_svm = clf.predict(X_test)
def SklearnVotingClassifier(X_train,Y_train,X_test,Y_test):
"""
:type X_train: numpy.ndarray
:type X_test: numpy.ndarray
:type Y_train: numpy.ndarray
:rtype: List[numpy.ndarray]
"""
#SVM (using linear kernel)
from sklearn import svm
clf = svm.SVC(kernel='linear')
clf.fit(X_train, Y_train)
print('Accuracy for SVM: ', Accuracy(Y_test,y_pred_svm))
#Logistic regression
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, Y_train)
y_pred_lr = lr.predict(X_test)
print('Accuracy for Logistic regression: ', Accuracy(Y_test,y_pred_lr))
#Decision Trees
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier.fit(X_train, Y_train)
y_pred_dt = classifier.predict(X_test)
print('Accuracy for Decision Tree: ', Accuracy(Y_test,y_pred_dt))
#K-nn
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=11)
knn.fit(X_train, Y_train)
y_pred_knn = knn.predict(X_test)
print('Accuracy for K-nn: ', Accuracy(Y_test,y_pred_knn))
list_all = []
list_all.append(y_pred_svm)
list_all.append(y_pred_lr)
list_all.append(y_pred_dt)
list_all.append(y_pred_knn)
return list_all
def SklearnVotingClassifier(X_train,Y_train,X_test,Y_test):
"""
:type X_train: numpy.ndarray
:type X_test: numpy.ndarray
:type Y_train: numpy.ndarray
:rtype: List[numpy.ndarray]
"""
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import VotingClassifier
kfold = model_selection.KFold(n_splits=5, random_state=5)
# creating the sub models
estimators = []
model1 = SVC(kernel='linear')
estimators.append(('svm', model1))
model2 = LogisticRegression()
estimators.append(('logistic', model2))
model3 = DecisionTreeClassifier()
estimators.append(('cart', model3))
model4 = KNeighborsClassifier()
estimators.append(('knn', model4))
# creating the ensemble model
ensemble_model = VotingClassifier(estimators, voting='hard')
ensemble_model.fit(X_train, Y_train)
y_pred_ensemble = ensemble_model.predict(X_test)
print('Accuracy for ensemble model: ', Accuracy(Y_test,y_pred_ensemble))
return y_pred_ensemble
"""
Create your own custom functions for Matplotlib visualization of hyperparameter search.
Make sure that plots are labeled and proper legends are used
"""
def VisualizationConfusionMatrix(Y_test, y_pred):
import matplotlib.pyplot as plt
%matplotlib inline
labels = np.unique(np.concatenate((Y_test,y_pred))).tolist()
cm = ConfusionMatrix(Y_test,y_pred)
print(cm)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
plt.title('Confusion matrix of the classifier')
fig.colorbar(cax)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()
def GridSearchCV_hp_tuning(X_train, X_test, y_train, y_test):
##for SVM for best parameters
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
param_grid = {'C': [1, 10],
'gamma': ('auto','scale'),
'kernel': ['linear']}
grid = GridSearchCV(SVC(), param_grid, cv=2)
grid.fit(X_train, y_train)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = grid.predict(X_test)
acc = Accuracy(grid_predictions, y_test)
print('Acc: ', acc)
print(classification_report(y_test, grid_predictions))
##for Knn for best parameters
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
params_knn = {'n_neighbors': [2,3,4,5],
'weights': ['uniform'],
'metric': ['euclidean']
}
knn_grid = GridSearchCV(knn, params_knn, cv=3)
knn_grid.fit(X_train, y_train)
print('Best parameters: ', knn_grid.best_params_)
print('Best estimator: ', knn_grid.best_estimator_)
grid_predictions = knn_grid.predict(X_test)
acc = Accuracy(grid_predictions, y_test)
print('Acc: ', acc)
print(classification_report(y_test, grid_predictions))
##for Decision Tree for best parameters
from sklearn.tree import DecisionTreeClassifier as dt
clf = dt()
param_grid = {'max_depth':[1,2,3],
'min_samples_leaf':[1,2,3,4,5],
'min_samples_split':[2,3,4],
'criterion':['gini','entropy']
}
grid = GridSearchCV(clf, param_grid, cv=10)
grid.fit(X_train, y_train)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = grid.predict(X_test)
acc = Accuracy(grid_predictions, y_test)
print('Acc: ', acc)
print(classification_report(y_test, grid_predictions))
##Tuning the hyperparameters
##for SVM: parameter C
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
c_values = [0.1, 1, 10 , 100]
acc = []
for i in c_values:
param_grid = {'C': [i],
'gamma': ('auto','scale'),
'kernel': ['linear']}
grid = GridSearchCV(SVC(), param_grid, cv=2)
grid.fit(X_train, y_train)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = grid.predict(X_test)
acc_1 = Accuracy(grid_predictions, y_test)
acc.append(acc_1)
xi = list(range(len(c_values)))
plt.plot(xi, acc, marker='o', linestyle='--', color='r', label='acc')
plt.xlabel('C values',fontweight="bold",fontsize = 12)
plt.ylabel('accuracy',fontweight="bold",fontsize = 12)
plt.title("C vs accuracy for GridSearchCV SVM",fontweight="bold",fontsize = 16)
plt.xticks(xi, c_values)
plt.legend()
plt.show()
##for SVM: parameter kernel
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
kernel_values = ['linear', 'rbf']
acc_k = []
for i in kernel_values:
# defining parameter range
param_grid = {'C': [10],
'gamma': ('auto','scale'),
'kernel': [i]}
grid = GridSearchCV(SVC(), param_grid, cv=2)
grid.fit(X_train, y_train)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = grid.predict(X_test)
acc_1 = Accuracy(grid_predictions, y_test)
acc_k.append(acc_1)
xi = list(range(len(kernel_values)))
plt.plot(xi, acc_k, marker='o', linestyle='--', color='r', label='acc')
plt.xlabel('kernel',fontweight="bold",fontsize = 12)
plt.ylabel('accuracy',fontweight="bold",fontsize = 12)
plt.title("kernels vs accuracy for GridSearchCV SVM",fontweight="bold",fontsize = 16)
plt.xticks(xi, kernel_values)
plt.legend()
plt.show()
##for decision tree: parameter max_depth
from sklearn.tree import DecisionTreeClassifier as dt
max_depth_values = [1, 2, 3]
acc_dep = []
clf=dt()
for i in max_depth_values:
param_grid = {'max_depth':[i],
'min_samples_leaf':[1,2,3,4,5],
'min_samples_split':[2,3,4],
'criterion':['gini','entropy']}
grid = GridSearchCV(clf,param_grid, cv=10)
a = grid.fit(X_train, y_train)
y_pred = grid.predict(X_test)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = grid.predict(X_test)
acc = Accuracy(grid_predictions, y_test)
acc_dep.append(acc)
xi = list(range(len(max_depth_values)))
plt.plot(xi, acc_dep, marker='o', linestyle='--', color='r', label='acc')
plt.xlabel('max_depth values',fontweight="bold",fontsize = 12)
plt.ylabel('accuracy',fontweight="bold",fontsize = 12)
plt.title("max_depth vs accuracy for GridSearchCV Decision Tree",fontweight="bold",fontsize = 16)
plt.xticks(xi, max_depth_values)
plt.legend()
plt.show()
##for Knn: parameter K
knn = KNeighborsClassifier()
acc_knn = []
n_values = [2, 3, 4, 5]
for i in n_values:
params_knn = {'n_neighbors': [i],
'weights': ['uniform'],
'metric': ['euclidean']
}
knn_grid= GridSearchCV(knn, params_knn, cv=3)
knn_grid.fit(X_train, y_train)
print('Best parameters: ', grid.best_params_)
print('Best estimator: ', grid.best_estimator_)
grid_predictions = knn_grid.predict(X_test)
acc_1 = Accuracy(grid_predictions, y_test)
print('acc: ',acc_1)
acc_knn.append(acc_1)
xi = list(range(len(n_values)))
plt.plot(xi, acc_knn, marker='o', linestyle='--', color='r', label='acc')
plt.xlabel('k values',fontweight="bold",fontsize = 12)
plt.ylabel('accuracy',fontweight="bold",fontsize = 12)
plt.title("k vs accuracy for GridSearchCV Knn",fontweight="bold",fontsize = 16)
plt.xticks(xi, n_values)
plt.legend()
plt.show()