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# =============================================================================
# Adding predicitve variables
# =============================================================================
''' Adding age '''
# Reference date
reference_date = datetime.date(2017, 5, 1)
# Add age to the basetable
basetable["age"] = (pd.Series([calculate_age(date_of_birth, reference_date)
for date_of_birth in basetable["date_of_birth"]]))
# Calculate mean age
print(round(basetable["age"].mean()))
''' Adding the donor segment '''
# basetable
'''
donor_id
0 98304
1 4
2 32778
... ...
9488 65524
9489 98293
9490 32759
'''
# segments
'''
donor_id segment
1379 69844 silver
1191 36363 gold
681 67425 bronze
... ... ...
9153 97124 silver
6847 56218 silver
6134 53780 silver
'''
# Add the donor segment to the basetable
# Left join（right join 就只有交集，沒有 NaN）
basetable = pd.merge(basetable, segments, on =["donor_id"], how="left")
'''
donor_id segment
0 98304 NaN
1 4 NaN
2 32778 bronze
... ... ...
9488 65524 gold
9489 98293 silver
9490 32759 NaN
'''
# Count the number of donors in each segment
basetable.groupby("segment").size()
# Count the number of donors with no segment assigned
print(basetable["segment"].isna().sum())
''' Adding living place '''
# living_places
'''
donor_ID start_date end_date living_place
0 32768 1989-03-25 2000-07-06 India
1 32768 2000-07-06 2099-01-01 UK
2 98305 1954-04-06 1958-08-12 USA
... ... ... ... ...
28803 32765 1998-03-09 2005-01-30 UK
28804 32765 2005-01-30 2012-11-28 UK
28805 32765 2012-11-28 2099-01-01 India
'''
# Reference date
reference_date = datetime.date(2017, 5, 1)
# Select living place reference date
living_places_reference_date = living_places[(living_places["start_date"] <= reference_date) &
(living_places["end_date"] > reference_date)]
# Add living place to the basetable
basetable = pd.merge(basetable, living_places_reference_date[["donor_ID", "living_place"]], on="donor_ID")
# =============================================================================
# Adding aggregated variables
# =============================================================================
''' Maximum value last year '''
# basetable
'''
donor_ID
0 3
1 5
2 6
'''
# gifts
'''
id date amount
0 1 2015-10-16 75.0
1 1 2014-02-11 111.0
2 1 2012-03-28 93.0
'''
# Start and end date of the aggregation period
start_date = datetime.date(2017, 1, 1)
end_date = datetime.date(2017, 5, 1)
# Select gifts made in 2017 and before May 1st
gifts_2017 = gifts[(gifts["date"] >= start_date) & (gifts["date"] < end_date)]
# Maximum gift per donor in 2017
gifts_2017_bydonor = gifts_2017.groupby(["id"])["amount"].max().reset_index()
gifts_2017_bydonor.columns = ["donor_ID", "max_amount"]
# Add maximum amount to the basetable
basetable = pd.merge(basetable, gifts_2017_bydonor)
''' Recency of donations '''
# Reference date to calculate the recency
reference_date = datetime.date(2017, 5, 1)
# Select gifts made before the reference date
gifts_before_reference = gifts[(gifts["date"] < reference_date)]
# Latest gift per donor in 2017
last_gift = gifts_before_reference.groupby(["id"])["date"].max().reset_index()
last_gift["recency"] = reference_date - last_gift["date"]
# Add recency to the basetable
basetable = pd.merge(basetable, last_gift[["id", "recency"]], how="left")
print(basetable)
'''
id recency
0 3 396 days
1 5 291 days
2 6 414 days
... ... ...
8508 29983 NaT
8509 29986 NaT
8510 29991 NaT
'''
# =============================================================================
# Adding evolutions
# =============================================================================
''' Ratio of last month's and last year's average '''
# Given are gifts_last_month (donors who donated last month) and gifts_last_year (donors who donated last year)
# Average gift last month for each donor
average_gift_last_month = gifts_last_month.groupby("id")["amount"].mean().reset_index()
average_gift_last_month.columns = ["donor_ID", "mean_gift_last_month"]
# Average gift last year for each donor
average_gift_last_year = gifts_last_year.groupby("id")["amount"].mean().reset_index()
average_gift_last_year.columns = ["donor_ID", "mean_gift_last_year"]
# Add average gift last month and year to basetable
basetable = pd.merge(basetable, average_gift_last_month, on="donor_ID", how="left")
basetable = pd.merge(basetable, average_gift_last_year, on="donor_ID", how="left")
# Calculate ratio of last month's and last year's average
basetable["ratio_month_year"] = basetable["mean_gift_last_month"] / basetable["mean_gift_last_year"]
''' Absolute difference between two years '''
# Number of gifts in 2016 and 2017 for each donor
gifts_2016_bydonor = gifts_2016.groupby("id")["amount"].count().reset_index()
gifts_2016_bydonor.columns = ["donor_ID", "donations_2016"]
gifts_2017_bydonor = gifts_2017.groupby("id")["amount"].count().reset_index()
gifts_2017_bydonor.columns = ["donor_ID", "donations_2017"]
# Add number of gifts in 2016 and 2017 to the basetable
basetable = pd.merge(basetable, gifts_2016_bydonor, on="donor_ID", how="left")
basetable = pd.merge(basetable, gifts_2017_bydonor, on="donor_ID", how="left")
# Calculate the number of gifts in 2017 minus number of gifts in 2016
basetable.fillna(0)
basetable["gifts_2017_min_2016"] = basetable["donations_2017"] - basetable["donations_2016"]
print(basetable.head())
# =============================================================================
# Using evolution variables
# =============================================================================
''' Performance of evolution variables '''
# variables_regular = ["gender_F", "age", "donations_2017"]
# variables_evolution = ["gender_F", "age", "donations_2017_min_2016"]
# Select the evolution variables and fit the model
X_evolution = basetable[variables_evolution]
logreg.fit(X_evolution, y)
# Make predictions and calculate the AUC
predictions_evolution = logreg.predict_proba(X_evolution)[:,1]
auc_evolution = roc_auc_score(y, predictions_evolution)
# Print the respective AUC values（假設 auc_regular 已經算好了）
print(round(auc_regular, 2)) # 0.6
print(round(auc_evolution, 2)) # 0.7
''' Meaning of evolution: plot the pig table '''
# Discretize the variable in 5 bins and add to the basetable
basetable["donations_2017_min_2016_disc"] = pd.qcut(basetable["donations_2017_min_2016"], 5)
# Construct the predictor insight graph table
pig_table = create_pig_table(basetable, "target", "donations_2017_min_2016_disc")
# Plot the predictor insight graph
plot_pig(pig_table, "donations_2017_min_2016_disc")
# Write a function that generates one of 3 numbers according to given probabilities
# https://www.geeksforgeeks.org/write-a-function-to-generate-3-numbers-according-to-given-probabilities/
# Given a function rand(a, b) which generate equiprobable random numbers between [a, b] inclusive. Generate 3 numbers x, y, z with probability P(x), P(y), P(z) such that P(x) + P(y) + P(z) = 1 using the given rand(a, b) function
# Let the given probabilities be in percentage form, like P(x) = 40%, P(y) = 25%, P(z) = 35%.
# Following steps:
# 1. Generate a random number between 1 to 100. Since they are equiprobable, the probability of each number is 1/100
# 2. Following are some important points to note about generated random number 'r'
# a. 'r' is smaller than or equal to P(x) with probability P(x) / 100
# b. 'r' is greater than P(x) and smaller than or equal P(x) + P(y) with P(y) / 100
# c. 'r' is greater than P(x) + P(y) and smaller than or equal 100 (or P(x) + P(y) + P(z)) with probability P(z) / 100
# This function generates 'x' with probability px/100, 'y' with probability py/100, and 'z' with probability pz/100; Assumption: px + py + pz = 100 where px, py and pz lie between 0 to 100
def random(x, y, z, px, py, pz):
# Generate a number from 1 to 100
import random
r = random.randint(1, 100)
# r is smaller than px with probability px/100
if r <= px:
return x
# r is greater than px and smaller than or equal to px+py with probability py/100
elif r <= px + py:
return y
# r is greater than px+py and smaller than or equal to 100 with probability pz/100
else:
return z
# Hello World program in Python
from math import *
N=int(input())
while(N>=0):
c=0
c = log(N,2)
c= c//1 if c%1==0 else 1 + c//1
c=int(c)
print(N,c)
N=int(input())
infile = open("qbdata.txt", "r")
line = infile.readline()
while line:
values = line.split()
print('QB ', values[0], values[1], 'had a rating of ', values[10] )
line = infile.readline()
infile.close()
#discribing the part of speech
adj = ["red", "big", "tasty","annoying"]
noun=["daniel","austrailia","chicken" ]
print ("as you now that a verb is a name")
for x in adj:
print(x)
#!/usr/bin/python3
import os, sys
# Open a file
path = "d:\\python3\\foo.txt"
fd = os.open( path, os.O_RDWR|os.O_CREAT )
# Close opened file
os.close( fd )
# Now get the touple
info = os.lstat(path)
print ("File Info :", info)
# Now get uid of the file
print ("UID of the file :%d" % info.st_uid)
# Now get gid of the file
print ("GID of the file :%d" % info.st_gid)
print = ("area of a triangle")
a=float(input("enter angle in degrees: \n"))
b=float(input("enter angle in degrees: \n"))1
c= round(180-a-b,1)
print ("the answer is ",c, "degrees")
if a==b and b==c and a==c:
print("this is an equilateral triangle")
elif a==90 or b==90 or c==90:
print("this is a right angle triangle")
elif a==b or b==c or a==c:
print("this is an isoceles triangle
")
