File size: 2,844 Bytes
b123f1a | 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 | import codecs,json
import numpy as np
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.model_selection import train_test_split
import pandas as pd
def create_bins(lower_bound, width, quantity):
bins = []
for low in range(lower_bound,
lower_bound + quantity*width + 1, width):
bins.append((low, low+width))
return bins
def find_bin(value, bins):
for i in range(0, len(bins)):
if bins[i][0] <= value <= bins[i][1]:
return i
print(value)
from collections import Counter
bins = create_bins(lower_bound = 15,
width = 7,
quantity=5)
# bins_len = [(0,15),(16, 22), (23, 29), (30, 36), (37, 43), (44, 50), (51, 57),(58,67),(68,100)]
bins_tree = [(0,2),(3,5),(6,8),(9,11),(12,20)]
bins = [(0,5),(6,8),(9,12),(13,16),(17,20),(21,25),(26,28),(29,1000)]
def getxy(task,language):
obj_text = codecs.open(f"./gold/{language}/multi_concat_tel_shifted.txt", 'r', encoding='utf-8').read()
d = json.loads(obj_text)
df = pd.read_csv(f"./gold/{language}/{task}.csv")
y = df[f"{task}"].to_numpy()
if task == "senlen":
for i in range(len(y)):
y[i] = find_bin(y[i],bins)
return d,y
# For Obj and Sub Number, data needs to be pruned before training the classifier
elif task == "subnum" or task =="objnum":
new_d = {}
for i in np.arange(12):
new_d[i] = []
for i in range(0,12):
for j in range(len(y)):
if y[j] == "sg" or y[j] == "pl":
new_d[i].append(d[str(i)][j])
y_new = []
for i in range(len(y)):
if(y[i] == "pl"):
y_new.append(1)
elif(y[i] == "sg"):
y_new.append(0)
y_new = np.array(y_new)
return new_d,y_new
# For WordContent, we need to change input data
elif task == "wordcontent":
new_d = {}
for i in np.arange(12):
new_d[i] = []
for i in range(0,12):
for j in range(len(y)):
new_d[i].append(d[str(i)][df["index"][j]])
return new_d,y
else:
# print(len(d[0]))
# print(len(y))
# exit(0)
return d,y
tasks = ["senlen","objnum","subnum","treedepth","wordcontent","bshift"]
# temp = ["str","no","no","str","no","str"]
task = tasks[5]
language = "telugu"
d,y = getxy(task,language)
# print(len(d))
# print(len(y))
# exit(0)
for i in range(0,12):
X_train, X_test, y_train, y_test = train_test_split(d[str(i)], y, test_size=0.2, random_state=42)
clf = LogisticRegression(random_state=0, multi_class = "multinomial",max_iter = 250).fit(X_train, y_train)
# clf = LogisticRegressionCV(cv=5,random_state=0,max_iter=1000).fit(np.array(d[i]), y)
print(i, clf.score(X_test, y_test))
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