View souceMultiLabel.py
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import numpy as np # linear algebra | |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import confusion_matrix | |
from sklearn.metrics import roc_auc_score | |
from sklearn.metrics import classification_report | |
from sklearn.datasets import make_multilabel_classification | |
from xgboost import XGBClassifier | |
from sklearn.model_selection import KFold |
View cr.py
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cr_y1 = classification_report(ytest[:,0],yhat[:,0]) | |
cr_y2 = classification_report(ytest[:,1],yhat[:,1]) | |
cr_y3 = classification_report(ytest[:,2],yhat[:,2]) | |
cr_y4 = classification_report(ytest[:,3],yhat[:,3]) | |
cr_y5 = classification_report(ytest[:,4],yhat[:,4]) | |
print (cr_y1) | |
----------------------------- | |
precision recall f1-score support |
View cm.py
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cm_y1 = confusion_matrix(ytest[:,0],yhat[:,0]) | |
cm_y2 = confusion_matrix(ytest[:,1],yhat[:,1]) | |
cm_y3 = confusion_matrix(ytest[:,2],yhat[:,2]) | |
cm_y4 = confusion_matrix(ytest[:,3],yhat[:,3]) | |
cm_y5 = confusion_matrix(ytest[:,4],yhat[:,4]) | |
print (cm_y1) | |
--------------- | |
[[1053 140] |
View accuracy.py
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auc_y1 = roc_auc_score(ytest[:,0],yhat[:,0]) | |
auc_y2 = roc_auc_score(ytest[:,1],yhat[:,1]) | |
auc_y3 = roc_auc_score(ytest[:,2],yhat[:,2]) | |
auc_y4 = roc_auc_score(ytest[:,3],yhat[:,3]) | |
auc_y5 = roc_auc_score(ytest[:,4],yhat[:,4]) | |
print("ROC AUC y1: %.4f, y2: %.4f, y3: %.4f, y4: %.4f, y5: %.4f" % (auc_y1, auc_y2, auc_y3, auc_y4, auc_y5)) | |
------------------------------------------------------- | |
ROC AUC y1: 0.8230, y2: 0.8025, y3: 0.8091, y4: 0.8005, y5: 0.8086 |
View XGBMulti.py
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classifier = MultiOutputClassifier(XGBClassifier()) | |
clf = Pipeline([('classify', classifier)]) | |
print (clf) | |
------------------------------------------------ | |
Pipeline(steps=[('classify', | |
MultiOutputClassifier(estimator=XGBClassifier(base_score=None, | |
booster=None, |
View dataoutputex.py
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for i in range(5): | |
print(x[i]," =====> ", y[i]) | |
---------------------------------------------------------------------------------- | |
[5. 4. 0. 4. 3. 0. 1. 1. 0. 3. 0. 1. 6. 0. 0. 2. 0. 1. 6. 1.] =====> [1 0 0 0 0] | |
[2. 2. 0. 1. 5. 1. 2. 0. 7. 4. 1. 0. 2. 1. 5. 2. 0. 4. 0. 6.] =====> [0 0 0 0 1] | |
[3. 4. 2. 1. 4. 5. 2. 2. 4. 1. 1. 2. 3. 5. 2. 3. 0. 4. 5. 2.] =====> [0 1 0 1 0] | |
[0. 5. 2. 3. 2. 3. 7. 4. 4. 1. 3. 0. 5. 5. 2. 1. 3. 3. 2. 3.] =====> [0 0 0 0 0] | |
[3. 6. 2. 3. 2. 0. 1. 3. 2. 4. 0. 0. 3. 4. 1. 6. 0. 5. 0. 8.] =====> [1 0 0 0 1] |
View multilabel_load.py
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import numpy as np # linear algebra | |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import confusion_matrix | |
from sklearn.metrics import roc_auc_score | |
from sklearn.metrics import classification_report | |
from sklearn.datasets import make_multilabel_classification | |
from xgboost import XGBClassifier | |
from sklearn.model_selection import KFold |
View LPS.py
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def longestPalindrome(s): | |
return find_LPS_recursive(st, 0, len(st)-1) | |
def find_LPS_recursive(s, s_Index, e_Index): | |
if s_Index > e_Index: | |
return 0 | |
if s_Index = endIndex: | |
return 1 |
View fibonacci.py
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# Basic Solution | |
def calFib(n): | |
if n < 2: | |
return n | |
return calFib(n-1) + calFib(n-2) | |
# Top-down DP with Memorization | |
def calculateFibonacci(n): |
View unbound_bottom.py
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def unbound_knapsack(profits, weights, capacity): | |
n = len(profits) | |
# base case | |
if capacity <= 0 or n == 0 or len(weights) != n: | |
return 0 | |
dp = [[-1 for _ in range(capacity+1)] for _ in range(len(profits))] | |
# initiaite the capacity = 0 columns |
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