markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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--- 과거작업 [1] Rename | # 1) 경로설정
from google.colab import drive
drive.mount('/content/gdrive')
%cd /content/gdrive/MyDrive/객체탐지/dataset
# 2) 필요한 라이브러리 임포트
import os
import torch
from IPython.display import Image
import os
import random
import shutil
from sklearn.model_selection import train_test_split
import xml.etree.ElementTree as E... | _____no_output_____ | MIT | yolov5/YOLOv5_Task1.ipynb | sosodoit/yolov5 |
[2] XmlToTxt- https://github.com/Isabek/XmlToTxt [3] Devide [4] 사진 거르기class 1,2,3,4 하나라도 있으면 살리고 없으면 제거. | # 1) 경로설정
%cd /content/gdrive/MyDrive/객체탐지/dataset
# 2) 필요한 라이브러리 임포트
import os
from glob import glob
img_list = glob('labels/train/*.txt')
val_img_list = glob('labels/val/*.txt')
print(len(img_list))
print(len(val_img_list))
four = [] #체크를 위한 리스트 생성
## 여기서 val_img_list / img_list 두개를 변경하면서 내부의 다른 index를 제거해주어야... | _____no_output_____ | MIT | yolov5/YOLOv5_Task1.ipynb | sosodoit/yolov5 |
1806554 Ganesh Bhandarkar (DA LAB RECORD) LAB 1 | print("Hello World")
date()
print(mean(1:5))
x <- 1
x
apple <-c('red','green',"yellow")
print(apple)
print(class(apple))
list1 <-list(c(2,5,3),21.3,sin)
M = matrix(c('a','a','b','c','b','a'),nrow=2,ncol=3,byrow =TRUE)
apple_colors <-c('green','green','yellow','red','red','red','green')
factor_apple <-factor(apple_co... | [1] "Hello World"
| MIT | DA Lab/1806554_da_lab_records_all.ipynb | ganeshbhandarkar/College-Labs-And-Projects |
Batch processing with Argo WorfklowsIn this notebook we will dive into how you can run batch processing with Argo Workflows and Seldon Core.Dependencies:* Seldon core installed as per the docs with an ingress* Minio running in your cluster to use as local (s3) object storage* Argo Workfklows installed in cluster (and ... | !kubectl get secret minio -n minio-system -o json | jq '{apiVersion,data,kind,metadata,type} | .metadata |= {"annotations", "name"}' | kubectl apply -n default -f - | secret/minio created
| Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Install Argo WorkflowsYou can follow the instructions from the official [Argo Workflows Documentation](https://github.com/argoproj/argoquickstart).You also need to make sure that argo has permissions to create seldon deployments - for this you can just create a default-admin rolebinding as follows: | !kubectl create rolebinding default-admin --clusterrole=admin --serviceaccount=default:default | rolebinding.rbac.authorization.k8s.io/default-admin created
| Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Create some input for our modelWe will create a file that will contain the inputs that will be sent to our model | mkdir -p assets/
with open("assets/input-data.txt", "w") as f:
for i in range(10000):
f.write('[[1, 2, 3, 4]]\n') | _____no_output_____ | Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Check the contents of the file | !wc -l assets/input-data.txt
!head assets/input-data.txt | 10000 assets/input-data.txt
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
[[1, 2, 3, 4]]
| Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Upload the file to our minio | !mc mb minio-seldon/data
!mc cp assets/input-data.txt minio-seldon/data/ | [m[32;1mBucket created successfully `minio-seldon/data`.[0m
...-data.txt: 146.48 KiB / 146.48 KiB ┃▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓┃ 2.14 MiB/s 0s[0m[0m[m[32;1m | Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Create Argo WorkflowIn order to create our argo workflow we have made it simple so you can leverage the power of the helm charts.Before we dive into the contents of the full helm chart, let's first give it a try with some of the settings.We will run a batch job that will set up a Seldon Deployment with 10 replicas and... | !helm template seldon-batch-workflow helm-charts/seldon-batch-workflow/ \
--set workflow.name=seldon-batch-process \
--set seldonDeployment.name=sklearn \
--set seldonDeployment.replicas=10 \
--set seldonDeployment.serverWorkers=1 \
--set seldonDeployment.serverThreads=10 \
--set batchWorker.wor... | [35mcreate-seldon-resource[0m: time="2020-08-06T07:21:48.400Z" level=info msg="Starting Workflow Executor" version=v2.9.3
[35mcreate-seldon-resource[0m: time="2020-08-06T07:21:48.404Z" level=info msg="Creating a docker executor"
[35mcreate-seldon-resource[0m: time="2020-08-06T07:21:48.404Z" level=info msg="Exec... | Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Check output in object storeWe can now visualise the output that we obtained in the object store.First we can check that the file is present: | import json
wf_arr = !argo get seldon-batch-process -o json
wf = json.loads("".join(wf_arr))
WF_ID = wf["metadata"]["uid"]
print(f"Workflow ID is {WF_ID}")
!mc ls minio-seldon/data/output-data-"$WF_ID".txt | [m[32m[2020-08-06 08:23:07 BST] [0m[33m 2.7MiB [0m[1moutput-data-401c8bc0-0ff0-4f7b-94ba-347df5c786f9.txt[0m
[0m | Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Now we can output the contents of the file created using the `mc head` command. | !mc cp minio-seldon/data/output-data-"$WF_ID".txt assets/output-data.txt
!head assets/output-data.txt
!argo delete seldon-batch-process | Workflow 'seldon-batch-process' deleted
| Apache-2.0 | examples/batch/argo-workflows-batch/README.ipynb | Syakyr/seldon-core |
Load data Read the csv file (first row contains the column names), specify the data types. | csv_dir = Path.cwd().parent / "data"
speeches_path = csv_dir / "all_speeches.txt"
dtypes={'title':'string', 'pages':'int64', 'date':'string', 'location':'string',
'highest_speaker_count':'int64', 'content':'string'}
df = pd.read_csv(speeches_path, header=0, dtype=dtypes)
df.head()
df.dtypes | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Dates Some dates had the year missing. The year for `Community_College_Plan` has a typo. | temp = df.loc[:, ['title','date']]
temp['has_year'] = temp.apply(lambda row: row['date'][-4:].isnumeric(), axis=1)
temp.loc[temp.has_year==False, :] | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Edit the dates that need to be corrected. | print(df.loc[df.title=='Community_College_Plan','date'])
df.loc[df.title=='Community_College_Plan','date'] = '9 January 2015'
print(df.loc[df.title=='Community_College_Plan','date'], '\n')
print(df.loc[df.title=='Recovery_and_Reinvestment_Act_2016','date'])
df.loc[df.title=='Recovery_and_Reinvestment_Act_2016','date']... | 78 9 January 20105
Name: date, dtype: string
78 9 January 2015
Name: date, dtype: string
256 26 February
Name: date, dtype: string
256 26 February 2016
Name: date, dtype: string
265 15 July
Name: date, dtype: string
265 15 July 2015
Name: date, dtype: string
| MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Parse the dates. | df['date'] = pd.to_datetime(df['date'], dayfirst=True, format="%d %B %Y")
df.head() | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
The `date` column now has type `datetime`. | df.dtypes | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Locations Locations that specify a specific place in the White House can be replaced by `White House, Washington D.C.`. | contains_WH = df.location.str.contains("White House", flags=re.I)
df.loc[contains_WH, 'location'] = "White House, Washington D.C." | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Make a `country`column, values for `White House` can already be filled. | df.loc[contains_WH, 'country'] = "USA"
df.loc[~contains_WH, 'country'] = "" | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Set country to `USA` for locations that contain state names or state abbreviations. In case it contains the abbreviation, replace it by the full state name. | states_full = ['Alabama','Alaska','Arizona','Arkansas','California','Colorado','Connecticut','Delaware','Florida','Georgia','Hawaii','Idaho','Illinois','Indiana','Iowa','Kansas','Kentucky','Louisiana','Maine','Maryland','Massachusetts','Michigan','Minnesota','Mississippi','Missouri','Montana','Nebraska','Nevada','New H... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Change `Washington, D.C.` (and some variations) to `Washington D.C.`. | df['location'] = df.location.str.replace("Washington, D.?C.?", repl="Washington D.C.", flags=re.I, regex=True) | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
If `country=='USA'`: We assume the last substring to be the state, the second to last the city and everything before that a more specific locations.If `country!='USA'`: We assume the last substring to be the country, the second to last the city and everything before that a more specific locations. | df.loc[:, 'count_commas'] = df.loc[:, 'location'].str.count(',')
df.loc[:,['location','country','count_commas']].sort_values(by='count_commas') | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
USA No commas | print(df.loc[(df.country=="USA") & (df.count_commas == 0), ['title','location']].sort_values(by='location'), '\n')
df.loc[df.title=='Ebola_CDC', ['state','city','specific_location']] = ['Georgia', 'Atlanta', 'no_specific_location']
df.loc[df.location=='Washington D.C.', ['state','city','specific_location']] = ['no_sta... | title location
308 Ebola_CDC Atlanta Georgia
258 State_of_the_Union_2012 Washington D.C.
278 Health_Care_Law_Signing Washington D.C.
284 White_House_Correspondents_Dinner_2015 Washington D.C.
285 ... | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
One comma + `Washington D.C.` | contains_WDC = df.location.str.contains("Washington D.C.", flags=re.I)
select = contains_WDC & (df.count_commas == 1)
print(df.loc[select, 'location'])
locations = df.loc[select, 'location'].str.extract(r"(.+), Washington D.C. *", flags=re.I)
df.loc[select, ['state','city']] = ['no_state', 'Washington D.C.']
df.loc[... | 1 Washington Hilton, Washington D.C.
5 Washington Hilton Hotel, Washington D.C.
8 White House, Washington D.C.
11 White House, Washington D.C.
12 White House, Washington D.C.
...
414 ... | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
One comma + other | select = (df.country=="USA") & ~contains_WDC & (df.count_commas == 1)
print(df.loc[select, 'location'])
states = df.loc[select, 'location'].str.extract(r".+?, *(.+)", flags=re.I)
cities = df.loc[select, 'location'].str.extract(r"(.+?), *.+", flags=re.I)
df.loc[select, 'state'] = states.values
df.loc[select, 'city'] ... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Some cities need corrections. Cities that don't have a space in them are all ok, we only need to look at ones with spaces. | contains = df.loc[select, 'city'].str.contains(" ", flags=re.I)
df.loc[select & contains, ['title', 'location','state','city','specific_location']].sort_values(
by= ['state','city','specific_location'])
need_corrections = ['Mayors_Conference_2015','White_House_Correspondents_Dinner_First','Beau_Biden_Eulogy',
... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Two commas | select = (df.country=="USA") & (df.count_commas == 2)
print(df.loc[select, 'location'])
states = df.loc[select, 'location'].str.extract(r".+?,.+?, *(.+)", flags=re.I)
cities = df.loc[select, 'location'].str.extract(r".+?, *(.+?),.+", flags=re.I)
locations = df.loc[select, 'location'].str.extract(r" *(.+?),.+?,.+", fla... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Some cities need corrections. | need_corrections = ['Obama-Romney_-_First_Live_Debate', 'NY_NJ_Explosions', 'Afghanistan_War_Troop_Surge',
'Tucson_Memorial_Address', 'Armed_Forces_Farewell']
states = ['Colorado', 'New York', 'New York', 'Arizona', 'Virginia']
cities = ['Denver', 'New York', 'West Point', 'Tucson', 'Arlington']
l... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Result for locations in USA | df.loc[df.country=="USA", ['location','country','state','city','specific_location']].sort_values(
by=['state','city','specific_location']) | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Not USA, but known location Note: some US locations don't have `country=='USA'` yet Zero commas | select = (df.country!="USA") & (df.location!="unknown_location") & (df.count_commas == 0)
df.loc[select, ['title','location']]
titles = df.loc[select, 'title']
df.loc[df.title=='Joint_Presser_with_President_Benigno_Aquino',
['country','state','city','specific_location']] = ['Philippines', 'no_state', 'Manila', ... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
One comma | select = (df.country!="USA") & (df.location!="unknown_location") & (df.count_commas == 1)
titles = df.loc[select, 'title']
df.loc[select, ['title','location']]
countries = df.loc[df.title.isin(titles), 'location'].str.extract(r".+?, *(.+)", flags=re.I)
cities = df.loc[df.title.isin(titles), 'location'].str.extract(r" *... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
some corrections | df.loc[df.title=='2004_DNC_Address',
['country','state','city','specific_location']] = ['USA', 'New York', 'Boston', 'no_specific_location']
df.loc[df.title=='Afghanistan_US_Troops_Bagram',
['country','state','city','specific_location']] = ['Afghanistan', 'no_state', 'Bagram', 'Bagram Air Field']
df.loc... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Two commas | select = (df.country!="USA") & (df.location!="unknown_location") & (df.count_commas == 2)
titles = df.loc[select, 'title']
df.loc[select, ['title','location']]
countries = df.loc[df.title.isin(titles), 'location'].str.extract(r".+?,.+?, *(.+)", flags=re.I)
cities = df.loc[df.title.isin(titles), 'location'].str.extract(... | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Three commas | select = (df.country!="USA") & (df.location!="unknown_location") & (df.count_commas == 3)
df.loc[select, ['title','location']]
df.loc[df.title=='UK_Young_Leaders',
['country','state','city','specific_location']] = ['England', 'no_state', 'London', 'Lindley Hall, Royal Horticulture Halls'] | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Result for locations not in USA | df.loc[(df.country!="USA") & (df.location!="unknown_location"), ['location','country','state','city','specific_location']].sort_values(
by=['country','state','city','specific_location']) | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
Unknown locations | print('There are %i unknown locations.' % len(df.loc[df.location=="unknown_location", :])) | There are 89 unknown locations.
| MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
drop location column Make new csv Only known locations | csv_dir = Path.cwd().parent / "speeches_csv"
path_only_known = csv_dir / "speeches_loc_known_cleaned.txt"
only_known = df.loc[df.location!="unknown_location", :]
only_known.to_csv(path_only_known, index=False, header=True, mode='w') | _____no_output_____ | MIT | components/notebooks/Clean_Up_Speeches.ipynb | jfsalcedo10/mda-kuwait |
15.077: Problem Set 3Alex Berke (aberke)From Rice, J.A., Mathematical Statistics and Data Analysis (with CD Data Sets), 3rd ed., Duxbury, 2007 (ISBN 978-0-534-39942-9). | %config Completer.use_jedi = False # autocomplete
import math
import numpy as np
import pandas as pd
import scipy.special
from scipy import stats
from sklearn.utils import resample
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina' | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Problems 10.48In 1970, Congress instituted a lottery for the military draft to support the unpopular war in Vietnam. All 366 possible birth dates were placed in plastic capsules in a rotating drum and were selected one by one. Eligible males born on the first day drawn were first in line to be drafted followed by tho... | lottery = pd.read_csv('1970lottery.txt')
lottery.columns = [c.replace("'", "") for c in lottery.columns]
lottery['Month'] = lottery['Month'].apply(lambda m: m.replace("'", ""))
lottery.head() | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
A. Plot draft number versus day number. Do you see any trend?No, a trend is not clear from a simple plot with these two variables. | _ = lottery.plot.scatter('Day_of_year', 'Draft_No', title='Day of year vs draft number') | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
B. Calculate the Pearson and rank correlation coefficients. What do they suggest?Both correlation coefficcients suggest there could be a relationship that is not immediately visible. | print('Pearson correlation coefficient: %0.4f' % (
stats.pearsonr(lottery['Draft_No'], lottery['Day_of_year'])[0]))
print('Spearman rank correlation coefficient: %0.4f' % (
stats.spearmanr(lottery['Draft_No'], lottery['Day_of_year'])[0])) | Pearson correlation coefficient: -0.2260
Spearman rank correlation coefficient: -0.2258
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
C. Is the correlation statistically significant? One way to assess this is via a permutation test. Randomly permute the draft numbers and find the correlation of this random permutation with the day numbers. Do this 100 times and see how many of the resulting correlation coefficients exceed the one observed in the dat... | iterations = 1000
pearson_coefficients = []
rank_coefficients = []
for i in range(iterations):
permuted_draft_no = np.random.permutation(lottery['Draft_No'])
pearson_coefficients += [stats.pearsonr(permuted_draft_no, lottery['Day_of_year'])[0]]
rank_coefficients += [stats.spearmanr(permuted_draft_no, lott... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
D. Make parallel boxplots of the draft numbers by month. Do you see any pattern? The mean draft numbers are lowest in the (later) months of November and December. | pd.DataFrame(
{m: lottery[lottery['Month']==m]['Draft_No'] for m in months.values()}
).boxplot(grid=False) | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
E. Examine the sampling variability of the two correlation coefficients (Pearson and rank) using the bootstrap (re-sampling pairs with replacement) with 100 (or 1000) bootstrap samples. How does this compare with the permutation approach?The results are drastically different from the results of the permutation ... | pearson_coefficients = []
rank_coefficients = []
for i in range(iterations):
resampled = resample(lottery)
pearson_coefficients += [stats.pearsonr(resampled['Draft_No'], resampled['Day_of_year'])[0]]
rank_coefficients += [stats.spearmanr(resampled['Draft_No'], resampled['Day_of_year'])[0]]
print('Pearson ... | Pearson correlation coefficient 95% CI for (1000) bootstrap samples:
(-0.2292, -0.2231)
Spearman rank correlation coefficient 95% CI for (1000) bootstrap samples:
(-0.2285, -0.2224)
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
11.15 Suppose that n measurements are to be taken under a treatment condition and another n measurements are to be taken independently under a control condition. It is thought that the standard deviation of a single observation is about 10 under both conditions. How large should n be so that a 95% confidence interval... | l = "676 88 206 570 230 605 256 617 280 653 433 2913 337 924 466 286 497 1098 512 982 794 2346 428 321 452 615 512 519".split(" ")
df = pd.DataFrame({
'test': l[::2],
'control': l[1::2],
}).astype(int)
df | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
A. Plot the differences versus the control rate and summarize what you see. | differences = df['test'] - df['control']
plt.scatter(df['control'], differences)
plt.xlabel('control values')
plt.ylabel('difference values')
_ = plt.title(' difference vs control values') | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
The difference values decrease as the control values increase. This relationship appears linear. B. Calculate the mean difference, its standard deviation, and a confidence interval. Let $D_i$ be the difference value.From Rice Section 11.3.1, a 100(1 − α)% confidence interval for $μ_D$ is$ \bar{D} \pm t_{n-1}(α/2)s_... | mean_D = differences.mean()
print('The mean difference ~ %s' % round(mean_D, 2))
n = len(differences)
std = np.sqrt((1/((n-1)*(n)))*(((differences - mean_D)**2).sum()))
print('The standard deviation ~ %s' % round(std, 2)) | The standard deviation ~ 202.53
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Since $n = 14$ and $t_{13}(0.025) = 2.160$, a 95% confidence interval for the mean difference is$ -461.29 \pm (437.46) = -461.29 \pm (2.160 \times 202.53) = \bar{D} \pm t_{n-1}(α/2)s_{\bar{D}}$ | print('95%% CI (%s , %s)' % (round(mean_D - (2.160 * std), 2), round(mean_D + (2.160 * std), 2))) | 95% CI (-898.76 , -23.81)
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
C. Calculate the median difference and a confidence interval and compare to the previous result. Based on Rice section 10.4: We can sort the values, and find the median and find confidence intervals around that median by using a binomial distribution. | sorted_differences = sorted(differences)
print('sorted difference values:', sorted_differences)
median = np.median(sorted_differences)
print('η: median value = %s' % median)
n = len(sorted_differences)
print('n = %s' % n) | sorted difference values: [-2480, -1552, -601, -587, -470, -375, -373, -364, -361, -163, -7, 107, 180, 588]
η: median value = -368.5
n = 14
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
We look to form a confidence interval of the following form:(X(k), X(n−k+1))The coverage probability of this interval isP(X(k) ≤ η ≤ X(n−k+1)) = = 1 − P(η X(n−k+1))The distribution of the number of observations greater than the median is binomial with n trials and probability $\frac{1}{2}$ of success on each trial. T... | binomials = []
for i in range(int(n/2)):
binomials += [((1/(2**n)) * sum([scipy.special.binom(n, j) for j in range(i+1)]))]
pd.Series(binomials).rename('P[X ≤ k]').to_frame() | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
P(X n−k+1)We can choose k = 4P(X 11) = 0.0287Since $2 \times 0.0287 = 0.0574$Then we have about a 94% confidence interval for (X(4), X(11)) which is: | print('(%s, %s)' % (sorted_differences[4], sorted_differences[11])) | (-470, 107)
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
In summary, the median vaue is -368.5 with a 94% CI of (-470, 107).In comparison, the mean value is approximately -461.29 with a 95% CI of (-898.76 , -23.81).The mean value is lower than the median value and it has a wider confidence interval due to its larger standard deviation. The mean value and its standard devia... | fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(15,5))
stats.probplot(differences.values, plot=ax0)
ax0.set_title('Normal probability plot: difference values')
ax1.hist(differences)
_ = ax1.set_title('Histogram: difference values') | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Using a t test:The null hypothesis is for no difference, i.e.$H_0: μ_𝐷 = 0 $The test statistic is then$ t = \frac{\bar{D} - μ_𝐷}{s_{\bar{D}}} = \frac{\bar{D}}{s_{\bar{D}}} $ which follows a t distribution with n - 1 degrees of freedom. | t = (np.abs(mean_D)/std)
print('t = %s' % round(t, 3)) | t = 2.278
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
From the t distribution table:$ t_{13}(0.025) = 2.160$ and $t_{13}(0.01) = 2.650 $ so the p-value of a two-sided test is less than .05 but not less than 0.02. Using The Signed Rank Test: | test_df = differences.rename('difference').to_frame()
test_df['|difference|'] = np.abs(test_df['difference'])
test_df = test_df.sort_values('|difference|').reset_index().drop('index', axis=1).reset_index().rename(
columns={'index':'rank'}
)[['difference', '|difference|', 'rank']]
test_df['rank'] = test_df['rank'] +... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
We now calculate W+ by summing the positive ranks. | positive_ranks = [w for w in test_df['signed rank'] if w > 0]
print('W+ = %s' % sum(positive_ranks)) | W+ = 17
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
From Table 9 of Appendix B, the two-sided test is significant at α = .05, but not at α = .02. The findings between the t test and The Unsigned Rank test are consistent. 11.46 The National Weather Bureau’s ACN cloud-seeding project was carried out in the states of Oregon and Washington. Cloud seeding was accomplished ... | control = pd.DataFrame({
'Type I': [
.0080, .0046, .0549, .1313, .0587, .1723, .3812, .1720, .1182, .1383, .0106, .2126, .1435,
],
'Type II': [
.0000, .0000, .0053, .0920, .0220, .1133, .2880, .0000, .1058, .2050, .0100, .2450, .1529,
],
})
seeded = pd.DataFrame({
'Type I': [
... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
First we compare the mean values of each sample with boxplots, and check for normality with normal probability plots. | fig, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4, figsize=(20,5))
fig.suptitle('Normal probability plots of values')
stats.probplot(control['Type I'], plot=ax0)
stats.probplot(control['Type II'], plot=ax1)
ax0.set_title('Control Type I')
ax1.set_title('Control Type II')
stats.probplot(seeded['Type I'], plot=ax2)
stats.pro... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
The boxplots do not show significant differences.The means of the seeded samples overlap with the values of their control counterparts. The seeded samples have longer tails.The normal probability plots show that the control data does not follow a normal distribution and the sample sizes are relatively small. ... | n = len(control)
m = len(seeded)
print('n = %s' % n)
print('m = %s' % m) | n = 13
m = 22
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
From Rice Section 11.2.3, a test statistic is calculated in the following way. First, we group all m + n observations together and rank them in order of increasing size.Let $n_1$ be the smaller sample size and let R be the sum of the ranks from that sample. Let $R′ = n_1(m + n + 1) − R$ and $R^* = min(R, R′)$.In our ... | type1 = pd.DataFrame({
'value': control['Type I'],
'sample': 'control',
}).append(pd.DataFrame({
'value': seeded['Type I'],
'sample': 'seeded',
})).sort_values('value').reset_index().drop('index', axis=1).reset_index().rename(
columns={'index':'rank'}
)
type1['rank'] += 1
R = type1[type1['sample']=... | R = 221
𝑅′= 𝑛1(𝑚+𝑛+1)−𝑅 = 247
𝑅* = 𝑚𝑖𝑛(𝑅,𝑅′) = 221
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Test for Type II:See computations below.There are 3 tied vaues of 0. We can assign them each the values of (1 + 2 + 3) / 3 = 2. However, since they are all in the same sample (control) this does not make a difference.𝑅* = 𝑚𝑖𝑛(𝑅,𝑅′) = 199From [these Wilcoxon Rank-Sum Tables](https://www.real-statistics.com/statis... | type2 = pd.DataFrame({
'value': control['Type II'],
'sample': 'control',
}).append(pd.DataFrame({
'value': seeded['Type II'],
'sample': 'seeded',
})).sort_values('value').reset_index().drop('index', axis=1).reset_index().rename(
columns={'index':'rank'}
)
type2['rank'] += 1
R = type2[type2['sample'... | R = 199
𝑅′= 𝑛1(𝑚+𝑛+1)−𝑅 = 269
𝑅* = 𝑚𝑖𝑛(𝑅,𝑅′) = 199
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
In general, there is weak evidence that seeding has an effect on either type of target. 13.24 Is it advantageous to wear the color red in a sporting contest? According to Hill and Barton (2005): Although other colours are also present in animal displays, it is specifically the presence and intensity of red colorat... | sports = pd.DataFrame({
'Sport': ['Boxing','Freestyle Wrestling', 'Greco Roman Wrestling', 'Tae Kwon Do'],
'Red': [148, 27, 25, 45,],
'Blue': [120, 24, 23, 35],
}).set_index('Sport')
sports
sports['Total'] = sports['Red'] + sports['Blue']
sports.loc['Total'] = sports.sum()
sports | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Some supplementary information is given in the file red-blue.txt. a. Let πR denote the probability that the contestant wearing red wins. Test the null hypothesis that πR = ½ versus the alternative hypothesis that πR is the same in each sport, but πR ≠ ½ .The null and alternative hypothesis setup models a game as a Be... | print('(447 choose 245) x (0.5)^447 = %s' % (scipy.special.comb(447, 245) * (0.5 ** 447)))
print('Z = %s' % ((245 - 223.5)/(np.sqrt(447*(0.5)*(0.5))))) | Z = 2.033830210313729
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
b. Test the null hypothesis πR = ½ against the alternative hypothesis that allows πR to be different in different sports, but not equal to ½.Again, games are modeled as bernoulli trials, but this time the bernoulli trials are independent across sports. Since the minimum n (total games) from each sport is reasonably la... | t = ( ((sports['Red'] - (0.5 * sports['Total']))**2) / (sports['Total'] * 0.5 * 0.5 )).sum()
print('chi-squared = %s' % t) | chi-squared = 8.571642380281773
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
C. Are either of these hypothesis tests equivalent to that which would test the null hypothesis πR = ½ versus the alternative hypothesis πR ≠ ½ using as data the total numbers of wins summed over all the sports?Yes, (A) was equivalent to this. D. Is there any evidence that wearing red is more favorable in so... | sports.drop('Total', axis=0).drop('Total', axis=1)
chi2, p, dof, ex = stats.chi2_contingency(sports.drop('Total', axis=0).drop('Total', axis=1))
assert(dof == 3)
print('chi-squared test statistic = %s' % chi2)
print('p = %s' % p)
print('\nexpected frequencies:')
pd.DataFrame(ex, columns=['Red','Blue']) | chi-squared test statistic = 0.3015017799642389
p = 0.9597457890114767
expected frequencies:
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
E. From an analysis of the points scored by winners and losers, Hill and Barton concluded that color had the greatest effect in close contests. Data on the points of each match are contained in the file red-blue.xls. Analyze this data and see whether you agree with their conclusion. Here we analyze the sports separate... | def get_red_blue_points_differences(points_fpath):
df =pd.read_csv(points_fpath)[
['Winner','Points Scored by Red', 'Points Scored by Blue']
].dropna()
df['|Difference|'] = np.abs(df['Points Scored by Red'] - df['Points Scored by Blue'])
return df
def plot_point_differences(df, name):
fig, a... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
It does appear that for each of the sports, *excluding* Freestyle Wrestling, the distribution of Red wins are skewed towards the smaller points differences, as compared to the Blue wins. We already saw from previous tests (parts A and B) that Red has a statistically significant higher chance of winning (p > 0.5). Howev... | def get_signed_ranks(df):
"""
Assigns signed ranks and computes W+
Returns W+, df
"""
df = df.sort_values('|Difference|').reset_index(drop=True)
# get the ranks, handling ties
differences = df['|Difference|']
ranks = []
r = []
for index, d in differences.items():
if (ind... | _____no_output_____ | MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Tae Kwon Doe | w, tkd = get_signed_ranks(tkd)
n = len(tkd)
exp_w = (n*(n+1))/4
var_w = (n*(n+1)*((2*n)+1))/24
z = (w - exp_w)/(np.sqrt(var_w))
print('n = %s' % n)
print('W+ = %s' % w)
print('E(W+) = %s' % exp_w)
print('Var(W+) = %s' % var_w)
print('Z = %s' % z)
tkd.head() | n = 70
W+ = 1292.0
E(W+) = 1242.5
Var(W+) = 29198.75
Z = 0.2896830397843113
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Boxing | w, boxing = get_signed_ranks(boxing)
n = len(boxing)
exp_w = (n*(n+1))/4
var_w = (n*(n+1)*((2*n)+1))/24
z = (w - exp_w)/(np.sqrt(var_w))
print('n = %s' % n)
print('W+ = %s' % w)
print('E(W+) = %s' % exp_w)
print('Var(W+) = %s' % var_w)
print('Z = %s' % z)
boxing.head() | n = 233
W+ = 13854.0
E(W+) = 13630.5
Var(W+) = 1060907.25
Z = 0.2169895498296029
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Greco Roman Wrestling | w, gr_wrestling = get_signed_ranks(gr_wrestling)
n = len(gr_wrestling)
exp_w = (n*(n+1))/4
var_w = (n*(n+1)*((2*n)+1))/24
z = (w - exp_w)/(np.sqrt(var_w))
print('n = %s' % n)
print('W+ = %s' % w)
print('E(W+) = %s' % exp_w)
print('Var(W+) = %s' % var_w)
print('Z = %s' % z)
gr_wrestling.head() | n = 51
W+ = 580.5
E(W+) = 663.0
Var(W+) = 11381.5
Z = -0.773311016598475
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Freestyle Wrestling | w, fw_wrestling = get_signed_ranks(fw_wrestling)
n = len(fw_wrestling)
exp_w = (n*(n+1))/4
var_w = (n*(n+1)*((2*n)+1))/24
z = (w - exp_w)/(np.sqrt(var_w))
print('n = %s' % n)
print('W+ = %s' % w)
print('E(W+) = %s' % exp_w)
print('Var(W+) = %s' % var_w)
print('Z = %s' % z)
fw_wrestling.head() | n = 54
W+ = 835.5
E(W+) = 742.5
Var(W+) = 13488.75
Z = 0.8007502737021251
| MIT | pset-3/pset3.ipynb | aberke/mit-stats-15.077 |
Merge Merging molecular systems A list of molecular systems are merged in to a new molecular system: | molsys_A = msm.build.build_peptide(['AceProNme',{'forcefield':'AMBER14', 'implicit_solvent':'OBC1'}])
molsys_B = msm.build.build_peptide(['AceValNme',{'forcefield':'AMBER14', 'implicit_solvent':'OBC1'}])
molsys_C = msm.build.build_peptide(['AceLysNme',{'forcefield':'AMBER14', 'implicit_solvent':'OBC1'}])
molsys_B = msm... | _____no_output_____ | MIT | docs/contents/basic/merge.ipynb | dprada/molsysmt |
Converting raster to vectorA cluster of functions that convert raster (.tiff) files generated as part of future scenario pipeline code, to vector (point shapefile) files.**Original code:** [Konstantinos Pegios](https://github.com/kopegios) **Conceptualization & Methodological review :** [Alexandros Korkovelos](https:/... | # Importing necessary modules
import geopandas as gpd
import rasterio as rio
import pandas as pd
import fiona
import gdal
import osr
import ogr
import rasterio.mask
import time
import os
import ogr, gdal, osr, os
import numpy as np
import itertools
import re
from rasterio.warp import calculate_default_transform, repro... | _____no_output_____ | MIT | agrodem_preprocessing/Future_Scenarios/Converting raster to vector.ipynb | babakkhavari/agrodem |
Raster (Re)projection to target CRSThis step is not necessary if the raster file is already in the target CRS | # Define project function
def reproj(input_raster, output_raster, new_crs, factor):
dst_crs = new_crs
with rio.open(input_raster) as src:
transform, width, height = calculate_default_transform(
src.crs, dst_crs, src.width*factor, src.height*factor, *src.bounds)
kwargs = src.meta.co... | _____no_output_____ | MIT | agrodem_preprocessing/Future_Scenarios/Converting raster to vector.ipynb | babakkhavari/agrodem |
Converting raster to shapefile | # Define functions
def pixelOffset2coord(raster, xOffset,yOffset):
geotransform = raster.GetGeoTransform()
originX = geotransform[0]
originY = geotransform[3]
pixelWidth = geotransform[1]
pixelHeight = geotransform[5]
coordX = originX+pixelWidth*xOffset
coordY = originY+pixelHeight*yOffset
... | 0 of 3580 rows processed
100 of 3580 rows processed
200 of 3580 rows processed
300 of 3580 rows processed
400 of 3580 rows processed
500 of 3580 rows processed
600 of 3580 rows processed
700 of 3580 rows processed
800 of 3580 rows processed
900 of 3580 rows processed
1000 of 3580 rows processed
1100 of 3580 rows proces... | MIT | agrodem_preprocessing/Future_Scenarios/Converting raster to vector.ipynb | babakkhavari/agrodem |
Assigning lat/long columns to the shapefile | # Import as geodataframe
path_shp = r"N:\Agrodem\Future_Scenarios\maize_cassava_scenarios\maize_cassava_scenarios\vectorfiles"
name_shp = "cassava_SG.shp"
future_crop_gdf = gpd.read_file(path_shp + "\\" + name_shp)
# Creating lon/lat columns
future_crop_gdf['lon'] = future_crop_gdf["geometry"].x
future_crop_gdf['lat'] ... | _____no_output_____ | MIT | agrodem_preprocessing/Future_Scenarios/Converting raster to vector.ipynb | babakkhavari/agrodem |
Exporting file back to shp or gpkg | # Define output path
path = r"N:\Agrodem\Future_Scenarios\maize_cassava_scenarios\maize_cassava_scenarios\vectorfiles"
name_shp = "cassava_SG.shp"
#dshp
future_crop_gdf.to_file(os.path.join(path,name_shp), index=False)
#gpkg
#future_crop_gdf.to_file("maize_BAU.gpkg", layer='Maize_Inputfile_Future', driver="GPKG") | _____no_output_____ | MIT | agrodem_preprocessing/Future_Scenarios/Converting raster to vector.ipynb | babakkhavari/agrodem |
强化学习第二章的例子的代码，10臂赌博问题，首先建立一个k臂赌博者的类。 | class Bandit:
'''参数：
kArm: int, 赌博臂的个数
epsilon: double, e-贪心算法的概率值
initial: 每个行为的行为的初始化估计
stepSize: double,更加估计值的常数步数
sampleAverages: if ture, 使用简单的均值方法替代stepSize权重更新
UCB: 不是None时，使用UCB算法,(初始值优化算法)
gradient: if ture, 使用算法的选择的基础标志(过去的均值作为基准，评价现在的值)
grad... | _____no_output_____ | BSD-2-Clause | EvaluativeFeedback/TenArmedTestbed.ipynb | xiaorancs/xr-reinforcement-learning |
Real Estate Modelling Project -Srini Objective -Build a model to predict house prices based on features provided in the dataset. -One of those parameters include understanding which factors are responsible for higher property value - $650K and above.-The data set consists of information on some 22,000 properties. -T... | import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import QuantileTransformer
from sklearn.linear_model import LinearRegression
from sk... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Fetching the data | df=pd.read_excel("Data_MidTerm_Project_Real_State_Regression.xls" ) # reading the excel file | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Checking the data type of the features for any corrections | df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 21597 entries, 0 to 21596
Data columns (total 21 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 21597 non-null int64
1 date 21597 non-null datetime64[ns]
2 be... | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Checking the column headers for case consistency and spacing for any corrections | col_list = df.columns
col_list
#filtered view of the repetitive house ids
repetitive_sales = df.groupby('id').filter(lambda x: len(x) > 1)
repetitive_sales | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Exploring the data | df['zipcode']= df['zipcode'].astype(str)
df.zipcode[df.zipcode.isin(["98102",
"98103",
"98105",
"98106",
"98107",
"98108",
"98109",
"98112",
"98115",
"98116",
"98117",
"98118",
"98119",
"98122",
"98125",
"98126",
"98133",
"98136",
"98144",
"98177",
"98178",
"98199"])] = "seattle_zipcode"
df.zipcode[df.zipcode.isin(["98... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Mapping a new variable called distance from the epicenter to see how far the properties are located to the main areas of Seatlle and Bellevue. | df['lat_long'] = tuple(zip(df.lat,df.long)) # creating one column with a tuple using latitude and longitude coordinates
df
df['zipcode']= df['zipcode'].astype(str)
seattle = [47.6092,-122.3363]
bellevue = [47.61555,-122.20392]
seattle_dist = []
for i in df['lat_long']:
seattle_dist.append(haversine((seattle),(i), u... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Applying Box-cox Powertransform | def plots (df, var, t):
plt.figure(figsize= (13,5))
plt.subplot(121)
sns.kdeplot(df[var])
plt.title('before' + str(t).split('(')[0])
plt.subplot(122)
p1 = t.fit_transform(df[[var]]).flatten()
sns.kdeplot(p1)
plt.title('after' + str(t).split('(')[0])
box_col = []
for item in df.colum... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Preparing the data | # x = df.drop("price", axis=1)
x = df_zips._get_numeric_data()
y = x['price']
x
for col in drop_list:
x.drop([col],axis=1,inplace=True) | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Modelling the data | y
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, random_state =1)
std_scaler=StandardScaler().fit(x_train)
x_train_scaled=std_scaler.transform(x_train)
x_test_scaled=std_scaler.transform(x_test)
x_train_scaled[0] | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Modeling using Statsmodels without scaling | x_train_const= sm.add_constant(x_train) # adding a constant
model = sm.OLS(y_train, x_train_const).fit()
predictions_train = model.predict(x_train_const)
x_test_const = sm.add_constant(x_test) # adding a constant
predictions_test = model.predict(x_test_const)
print_model = model.summary()
print(print_model) | OLS Regression Results
==============================================================================
Dep. Variable: price R-squared: 0.725
Model: OLS Adj. R-squared: 0.724
Meth... | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
checking the significant variables | model.params[list(np.where(model.pvalues < 0.05)[0])].iloc[1:].index.tolist()
significant_features=x[model.params[list(np.where(model.pvalues < 0.05)[0])].iloc[1:].index.tolist()]
model = LinearRegression()
model.fit(x_train, y_train)
coefficients = list(model.coef_)
coefficients | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
with scaling | x_train.columns
x_train_const_scaled = sm.add_constant(x_train_scaled) # adding a constant
model = sm.OLS(y_train, x_train_const_scaled).fit()
predictions_train = model.predict(x_train_const_scaled)
x_test_const_scaled = sm.add_constant(x_test_scaled) # adding a constant
predictions_test = model.predict(x_test_const... | OLS Regression Results
==============================================================================
Dep. Variable: price R-squared: 0.725
Model: OLS Adj. R-squared: 0.724
Meth... | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Linear regression | model=LinearRegression() # model
model.fit(x_train_scaled, y_train) # model train
y
y_pred=model.predict(x_test_scaled) # model prediction
y_pred_train=model.predict(x_train_scaled)
# Make an scatter plot y_pred vs y
# What kind of plot you will get if all the all the predictions are ok?
# A stright line
fig... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Model validation | train_mse=mse(y_train,y_pred_train)
test_mse=mse(y_test,y_pred)
print ('train MSE: {} -- test MSE: {}'.format(train_mse, test_mse)) | train MSE: 37602966924.11132 -- test MSE: 34361724727.49398
| MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
RMSE | print ('train RMSE: {} -- test RMSE: {}'.format(train_mse**.5, test_mse**.5)) | train RMSE: 193914.84451715223 -- test RMSE: 185369.1579726627
| MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
MAE | train_mae=mae(y_train,y_pred_train)
test_mae=mae(y_test,y_pred)
print ('train MAE: {} -- test MAE: {}'.format(train_mse, test_mse)) | train MAE: 37602966924.11132 -- test MAE: 34361724727.49398
| MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
R2 | #R2= model.score(X_test_scaled, y_test)
R2_train=r2_score(y_train,y_pred_train)
R2_test=r2_score(y_test,y_pred)
print (R2_train)
print(R2_test)
print ('train R2: {} -- test R2: {}'.format(model.score(x_train_scaled, y_train),
model.score(x_test_scaled, y_test))) | train R2: 0.7245706790336555 -- test R2: 0.7328942112790509
| MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
adjusted rsquare | Adj_R2_train= 1 - (1-R2_train)*(len(y_train)-1)/(len(y_train)-x_train.shape[1]-1)
Adj_R2_train
Adj_R2_test= 1 - (1-R2_test)*(len(y_test)-1)/(len(y_test)-x_test.shape[1]-1)
Adj_R2_test
features_importances = pd.DataFrame(data={
'Attribute': x_train.columns,
'Importance': abs(model.coef_)
})
features_importances ... | _____no_output_____ | MIT | source_code/MidTerm_Project_srini.V3_trials.ipynb | Denny-Meyer/IronHack_mid_term_real_state_regression |
Iteration Example | import pyblp
import numpy as np
pyblp.__version__ | _____no_output_____ | MIT | docs/notebooks/api/iteration.ipynb | yusukeaoki1223/pyblp |
In this example, we'll build a SQUAREM configuration with a $\ell^2$-norm and use scheme S1 from :ref:`references:Varadhan and Roland (2008)`. | iteration = pyblp.Iteration('squarem', {'norm': np.linalg.norm, 'scheme': 1})
iteration | _____no_output_____ | MIT | docs/notebooks/api/iteration.ipynb | yusukeaoki1223/pyblp |
Next, instead of using a built-in routine, we'll create a custom method that implements a version of simple iteration, which, for the sake of having a nontrivial example, arbitrarily identifies a major iteration with three objective evaluations. | def custom_method(initial, contraction, callback, max_evaluations, tol, norm):
x = initial
evaluations = 0
while evaluations < max_evaluations:
x0, (x, weights, _) = x, contraction(x)
evaluations += 1
if evaluations % 3 == 0:
callback()
if weights is None:
d... | _____no_output_____ | MIT | docs/notebooks/api/iteration.ipynb | yusukeaoki1223/pyblp |
We can then use this custom method to build a custom iteration configuration. | iteration = pyblp.Iteration(custom_method)
iteration | _____no_output_____ | MIT | docs/notebooks/api/iteration.ipynb | yusukeaoki1223/pyblp |
 ***A machine learning approach to predicting how badly you'll get roasted for your sub-par reddit comments.***Alex Hartford & Trevor Hacker **Dataset**Reddit comments from September, 2018 (source). This is well over 100gb of data. We will likely... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_squared_error
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklear... | Libraries loaded!
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
Import and Clean Data | print('Loading memes...')
# df = pd.read_csv('https://s3.us-east-2.amazonaws.com/redditscore/2500rows.csv')
df = pd.read_csv('https://s3.us-east-2.amazonaws.com/redditscore/2mrows.csv', error_bad_lines=False, engine='python', encoding='utf-8')
print('Memes are fully operational!')
print(df.dtypes)
print()
print(df.sh... | subreddit object
body object
score float64
dtype: object
(1961645, 3)
| MIT | python/redditscore.ipynb | AlexHartford/redditscore |
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