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ball_df.dropna(inplace=True)
print(ball_df)

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from sklearn.metrics import classification_report
y_pred = rfc.predict(x_val)
print(classification_report(y_val,y_pred))

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from sklearn.ensemble import RandomForestClassifier 
rfc = RandomForestClassifier(max_depth=3) 
rfc.fit(x_tr,y_tr)

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from sklearn.model_selection import train_test_split
x_tr,x_val,y_tr,y_val = train_test_split(features,labels, test_size=0.2, stratify=labels,random_state=0)

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img_copy = np.copy(gray)
cv2.drawContours(img_copy, contours, -1, (0,255,0), 3)
plt.imshow(img_copy, cmap='gray')

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image, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

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i=0
while(True):

    #compute frequency
    pairs = get_stats(oov)

    #extract keys
    pairs = pairs.keys()
    
    #find the pairs available in the learned operations

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#applying BPE to OOV
oov ='lowest'

#tokenize OOV into characters
oov = " ".join(list(oov))

#append </w> 
oov = oov + ' </w>'

#create a dictionary

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num_merges = 10
for i in range(num_merges):
    
    #compute frequency of bigrams in a corpus
    pairs = get_stats(corpus)
    
    #compute the best pair
    best = max(pairs, key=pairs.get)
    
    #merge the frequent pair in corpus

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#merge the frequent pair in corpus
corpus = merge_vocab(best, corpus)
print("After Merging:", corpus)

#convert a tuple to a string
best = "".join(list(best))

#append to merge list and vocabulary
merges = []
merges.append(best)