しめくくり

 今年は本当に大変だった。

6月だか7月に、JAEAの受け入れは難しいみたいな話になり、就活開始。
（実際辞めるとなったら真逆のお返事が返ってきましたが、いまさらという話である。訴えられないためですかね？）

秋口に妻の妊娠がわかって、来年の4月に出産予定でそれまでここから動けないので、滞在許可とか保険をどうするかとかなんだかんだしているうちに、年末になった。
あと、年齢的なこともあり、流産などのリスクのことを考えると、特に初めのうちは本当に気が気ではなかった。

経済的には多少厳しいがめどは立ったと思うので、年を越す気分には何とかなれた。

job offer出してくださった会社にはいろいろご迷惑をおかけしてしまった。

Linkedinで声をかけてくれたはいいけど途中で音信不通になった会社が結構あったり、将来の直属の上司は何とかしようとしてくれたのに会社がダメと言ってきて全部潰れたこともありつつ、オーストリア国外の会社なのに、ここでの社会保障（というか、具体的に健康保険）のために余分なお金を使っても抱えてくれようとしたところがあらわれたので、かなり救われた感じ。

まだ、終わってない（３０日である）けれども、明日は何も起こるまい。

My algorithm, SUP-GMRESR, has been merged into the main branch of FrontISTR

Finally, my algorithm about a Krylov-type linear equation solver, has been merged into the main branch of FrontISTR

The algorithm can be found here.

A good Christmas gift!

Invited talk

 Have just given an invited talk at the 4th FrontISTR symposium about my algorithm.

Probably my algorithm will be merged into it!

https://www.frontistr.com/event/?id=61

Published?

 https://doi.org/10.1142/S0219876222500414

Finally, the article was accepted!.

I was informed that it was accepted on the 3rd of August, but the website says it was on the 23rd.

Something is not quite correct, but errors will be fixed.

Revision submitted

hope it comes back to me soon! 

Decision in Process

Decision in Process@17/May/2022 

Now "under review"

It is now "Under Review". Needed a couple of days after it went to "With Editor" 

update on my article

After two weeks of the submission, finally my article is "with editor".

I have thought it would go directly to "under review".

 

A new paper, but "submitted to Journal" for over a week

 A new paper was submitted on 25th of Feb., but still "submitted to journal". nearly two weeks.

hope an editor is handling my manuscript, but no reviewer is assigned yet.

It was submitted to International Journal of Computational Methods

House Price@Kaggle with TensorFlow

Because I completed a Coursera tensorflow course, I tried a Kaggle quiz.

House Prices - Advanced Regression Techniques

Probably there is a lot of room to tune, but at least this code works.

I know this is super awkward, but at the same time, this may help someone.

import pandas as pd

import numpy as np

import tensorflow as tf

FEATURES = []

FEAT_CONT=["LotFrontage", "LotArea", "OverallQual", "OverallCond", "YearBuilt", "YearRemodAdd", "MasVnrArea", "TotalBsmtSF",

"1stFlrSF", "2ndFlrSF", "LowQualFinSF", "GrLivArea", "BsmtFullBath", "BsmtHalfBath", "FullBath", "HalfBath",

"TotRmsAbvGrd", "Fireplaces", "GarageYrBlt", "GarageCars", "GarageArea", "WoodDeckSF", "OpenPorchSF", "EnclosedPorch", "3SsnPorch",

"ScreenPorch", "PoolArea", "MiscVal"]

FEAT_CAT = [ "MSSubClass", "MSZoning", "Street", "Alley", "LotShape", "LandContour", "Utilities", "LotConfig", "LandSlope", "Neighborhood",

"Condition1", "Condition2", "BldgType", "HouseStyle", "RoofStyle", "RoofMatl", "Exterior1st", "Exterior2nd",

"MasVnrType", "ExterQual", "ExterCond", "Foundation", "Heating", "HeatingQC", "CentralAir", "Electrical", "KitchenQual", "Functional",

"FireplaceQu", "GarageType", "GarageFinish", "GarageQual", "GarageCond", "PavedDrive", "PoolQC", "Fence", "MiscFeature",

"SaleType", "SaleCondition"]

def get_input_fn(data_set, num_epochs=None, shuffle=True):

  return tf.compat.v1.estimator.inputs.pandas_input_fn(

      x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),

      y=pd.Series(data_set["SalePrice"].values),

      num_epochs=num_epochs,

      shuffle=shuffle)

prediction_set = pd.read_csv("test.csv")

prediction_set.dropna(how='all', axis=1,inplace=True)

prediction_set.fillna(0,inplace = True)

prediction_set = prediction_set.drop(["Id"],axis=1)

prediction_set["SalePrice"]=0.0

for i in FEAT_CAT:

    prediction_set[i][prediction_set[i]==0]="0"

training_set = pd.read_csv("train.csv")

training_set.dropna(how='all', axis=1,inplace=True)

training_set.fillna(0,inplace = True)

training_set = training_set.drop(["Id"],axis=1)

for i in FEAT_CAT:

    training_set[i][training_set[i]==0]="0"

test_set     = training_set.iloc[1400:,:]

training_set = training_set.iloc[:1400,:]

FEATURES = FEAT_CONT + FEAT_CAT

feature_cols = [tf.feature_column.numeric_column(k) for k in FEAT_CONT]

feature_cat = []

for i in FEAT_CAT:

    data = training_set[i].values.tolist()

    data = set(data)

    feature_cat.append(tf.feature_column.embedding_column(tf.feature_column.categorical_column_with_vocabulary_list(key=i,vocabulary_list=tuple(data)),16 ))

numN = len(FEATURES)

regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols, hidden_units=[300,150,75,30], model_dir=None)

regressor.train(input_fn=get_input_fn(training_set), steps=5000)

ev = regressor.evaluate(input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))

loss_score = ev["loss"]

print("Loss: {0:f}".format(loss_score))

y = regressor.predict(input_fn=get_input_fn(test_set, num_epochs=1, shuffle=False))

pred = []

for i in y:

    pred.append((i["predictions"][0]))

test_result = test_set["SalePrice"].values.tolist()

for i in range(len(pred)):

    print(i,pred[i],test_result[i])

y = regressor.predict(input_fn=get_input_fn(prediction_set, num_epochs=1, shuffle=False))

pred = []

for i in y:

    pred.append((i["predictions"][0]))

id = 1460

with open("output.csv","w") as f:

   for i in range(len(pred)):

      line = str(id+i+1)+","+str(pred[i]) +"\n"

      f.write(line)

SPACK v0.17 on RH6

From v0.17, spack does not run on RH6.
The first reason is; python2.7 is needed, and the second; clingo needs to be installed by users, instead of bootstrapping.

I use miniconda for python2.7. Clingo can be installed via miniconda as
$ conda install -c potassco clingo

And also you may want to disable bootstraping as;

% spack bootstrap untrust github-actions

%  spack bootstrap disable

Please see
https://spack.io/changes-spack-v017/

Android 12 on Xiaomi A1

My A1 works on Pixel Experience Android 12. Looks good. Actually, feel better than Poco F3 with MIUI.