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Karthik Viswanathan nickinack

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View f16.py
import numpy as np
import matplotlib.pyplot as plt
import random
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn import metrics
View f15.py
df1 = pd.read_csv("communities.data" , header=None)
def read_header(filename):
'''
Given a filename containing headers, extract the headers and assign it to df
'''
header_list = []
with open(filename) as f:
for line in f:
if "@attribute" in line:
View f14.py
def drop_columns(columns , df):
'''
Given dataframe , returns updated df with removed colums
'''
for i in columns:
df = df.drop(i , axis=1)
return df
drop_list = ['state' , 'county' , 'community' , 'communityname' , 'fold' ]
View f13.py
x = df1.drop("ViolentCrimesPerPop", axis=1)
y = df1["ViolentCrimesPerPop"]
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=24)
print(x_train.shape , y_train.shape)
print(x_test.shape , y_test.shape)
View f12.py
def fitness_func(chromosome):
columns = []
for i in range(len(x_train.columns)):
if i in chromosome:
columns.append(x_train.columns[i])
dist.append(columns)
training_set = x_train[columns]
print(training_set)
test_set = x_test[columns]
lg = LinearRegression().fit(training_set.values, y_train.values)
View f11.py
class FeatureSolver(AbstractSolver):
def __init__(
self,
problem_type=float,
fitness_func= lambda a : fitness_func(a),
pop_cnt: int = 40,
gene_size: int = 50,
max_gen: int = 2,
mutation_ratio: float = 0.2,
selection_ratio: float = 0.2,
View f6.ipynb
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