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)