tutorial 3

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Oct 30, 2023

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+ DMA'23 - Tutorial 3: Decision Trees After this tutorial, you will be able to: 1. Use lambda functions 2. Use itertools and counters 3. Use classifiers (estimators) from sklearn import itertools from collections import Counter import pandas as pd import numpy as np from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy score v Lambda Function Lambdas are one line functions that are not named. # Define a lambda function that adds two numbers add = lambda x, y: X +y # Use the lambda function result = add(5, 3) print(result) 8 def add(x,y): return x+y <function <lambday> at 0x7e9049dbd750> v Write a function to find the reciprocal of a number. Let's create a dummy df with 100 rows and 5 columns: dummy data = pd.DataFrame(np.random.randint(1,100,size=(100, 5)), columns=1ist('ABCDE')) print(dummy data.shape) dummy_data.head()

(100, 5) A B C D E 99 77 40 41 24 29 80 31 53 43 63 77 2 a/R 74 7 79 QK We can create new columns by applying functions on existing columns dummy_data['E_1"'] dummy_data['E_2"'] pow(dummy data['E'],2) 1/dummy_data['E'] dummy_data.head() E_2 1681 0.024390 961 0.032258 5929 0.012987 7225 0.011765 4 68 97 38 97 9409 0.010309 def get reciprocal num(x): return 1 / x dummy_data['E_R'] = dummy data.head() dummy_data['E'].apply(get_reciprocal_num) 55 97 38 85 97 1681 961 5929 7225 9409 E_2 0.024390 0.032258 0.012987 0.011765 0.010309 E_R 0.024390 0.032258 0.012987 0.011765 0.010309 The above function can be written as a lambda function: dummy_data['E_R'] = dummy_data['E'].apply(lambda x: 1/x) dummy data.head()

A B C D E E1 E_2 E_R 0 8 99 77 40 41 1681 0.024390 0.024390 1 87 24 29 80 31 961 0.032258 0.032258 2 63 53 43 63 77 5929 0.012987 0.012987 3 96 79 78 78 85 7225 0.011765 0.011765 Functions can be applied: 1. Column wise (all rows of a single column go as inputs). Use axis=0 2. Row wise (all columns of a single row go as inputs). Use axis=1 axis : {@ or 'index', 1 or ‘'columns'}, default © Axis along which the function is applied: * @ or 'index': apply function to each column. * 1 or 'columns': apply function to each row. dummy_data_new = dummy_data.apply(lambda x:np.mean(x), axis = 0) print(type(dummy_data new)) dummy data_ new <class 'pandas.core.series.Series'> A 50.610000 B 51.040000 C 53.950000 D 48.840000 E 47.110000 E_1 3029.870000 E_2 ©.051245 E_R 0.051245 dtype: float64 dummy data new = dummy data.apply(lambda x:np.mean(x), axis=1) print(type(dummy_data_new)) dummy data_new <class 'pandas.core.series.Series'> %) 252.381098 1 151.508065 2 778.503247 3 955.127941 4 1220.502577 95 972.002907 96 30.285714 97 144.758333 98 98.635417 99 342.630000 Length: 100, dtype: float64

v ltertools and counters e jterable e chain e combinations i = iter(range(7)) #iterator(iterable - anything that would work in a for loop: list, dict print(type(i)) <class 'range_iterator'> print(next(i)) 0 print(next(i)) 1 print(next(i)) 2 print(i) <range_iterator object at 0x7e9049d8bde0d> 1 = list(i) print(1l) print(l.pop()) print(1l) [3.’ 4.’ 5.’ 6] 6 [3, 4, 5] v Reminder A combination is a mathematical technique that determines the number of possible arrangements in a collection of items where the order of the selection does not matter. e (7)o print("-- Combinations of 2--")

for combo in itertools.combinations(iter(range(5)), 2): print(combo) print("\n-- Combinations of 3--") for combo in itertools.combinations(iter(range(5)), 3): print(combo) -- Combinations of 2-- (0, 1) (0, 2) (0, 3) (0, 4) (1, 2) (1, 3) (1, 4) (2, 3) (2, 4) (3, 4) -- Combinations of 3-- (e, 1, 2) (e, 1, 3) (0, 1, 4) (e, 2, 3) (e, 2, 4) (0, 3, 4) (1, 2, 3) (1, 2, 4) (1, 3, 4) (2, 3, 4) zero_5 = range(0,6) six_10 = range(6,11) chained_iterator = itertools.chain(zero_5, six_10) for item in chained_iterator: print(item) OVooNOTUVhA, WNERO = o Power set: Contains all possible subsets of a set (plus the empty set and the original set). Example: If S is the set {X, y, z}, then all the subsets of S are:

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