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<!DOCTYPE HTML>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>MLE_ND_P6_SMC2</title>
<script src="https://sagecell.sagemath.org/static/embedded_sagecell.js"></script>
<script>$(function () {
sagecell.makeSagecell({inputLocation:'div.linked',linked:true,evalButtonText:'Run Linked Cells'});
sagecell.makeSagecell({inputLocation:'div.sage',evalButtonText:'Run'});});
</script>
</head>
<style>
@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');
body {margin:5px 5px 5px 15px; background-color:honeydew;}
a,p {color:forestgreen; font-family:'Roboto';}
h1 {color:#31c831; font-family:'Orbitron'; text-shadow:4px 4px 4px #ccc;}
h2,h3 {color:slategray; font-family:'Orbitron'; text-shadow:4px 4px 4px #ccc;}
h4 {color:#31c831; font-family:'Roboto';}
.sagecell .CodeMirror-scroll {min-height:3em; max-height:30em;}
.sagecell table.table_form tr.row-a {background-color:lightgray;}
.sagecell table.table_form tr.row-b {background-color:honeydew;}
.sagecell table.table_form td {padding:5px 15px; color:forestgreen; font-family:'Roboto';}
.sagecell_sessionOutput, .sagecell_sessionOutput pre {color:forestgreen; font-family:'Roboto';}
</style>
<body>
<h1>Machine Learning Engineer Nanodegree</h1>
<h2>Capstone Project</h2>
<h1>📑 P6: Sberbank Russian Housing Market. Part 2</h1>
<h2>Links and Code Library</h2>
<h3>Resources</h3>
<a href="https://scikit-learn.org/stable/index.html">🕸scikit-learn. Machine Learning in Python </a>
<a href="http://scipy-lectures.org/">🕸Scipy Lecture Notes </a><br/>
<h3>Versions</h3>
<a href="https://colab.research.google.com/drive/13YVEPwrMQC2TO2GGPvV95Bysh4dhd8we">🕸Colaboratory Version </a>
<a href="https://www.kaggle.com/olgabelitskaya/sberbank-russian-housing-market">🕸Kaggle Version </a><br/>
<h3>Code Library</h3>
<div class="linked"><script type="text/x-sage">
#unlock to install modules
#!python3 -m pip install keras --user
#!python3 -m pip install --ignore-installed --upgrade tensorflow==1.14.0
</script></div><br/>
<div class="linked"><script type="text/x-sage">
spath='/home/sc_work/.sage/local/lib/python3.7/site-packages'
import sys; sys.path.append(spath)
import warnings; warnings.filterwarnings("ignore")
from sklearn.exceptions import DataConversionWarning
warnings.filterwarnings("ignore",category=DataConversionWarning)
import os,zipfile,numpy as np,pandas as pd,scipy as sc
import pylab as pl,seaborn as sn,sympy as sp,keras as ks
pl.style.use('seaborn-whitegrid')
fw='weights.sberbank.hdf5'
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.feature_selection import SelectKBest,chi2,RFE
from sklearn.decomposition import PCA
from sklearn.metrics import mean_squared_error,median_absolute_error,\
mean_absolute_error,r2_score,explained_variance_score
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import BaggingRegressor,AdaBoostRegressor
from sklearn.ensemble import ExtraTreesRegressor,RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import LinearRegression,SGDRegressor,Ridge,\
RidgeCV,BayesianRidge,HuberRegressor,TheilSenRegressor,RANSACRegressor
from sklearn.preprocessing import OneHotEncoder,RobustScaler
from keras.models import Sequential,Model
from keras.layers import Conv1D,Conv2D,MaxPooling1D,MaxPooling2D
from keras.layers import GlobalAveragePooling2D,GlobalMaxPooling2D
from keras.layers.advanced_activations import PReLU,LeakyReLU
from keras.layers import Input,Dense,LSTM,Activation,Flatten,Dropout
</script></div><br/>
<h3>Set of Functions</h3>
<div class="linked"><script type="text/x-sage">
def display_cat(df,feature):
print ('\n'+'<_>'*20);
print ('\n'+feature+': '+str(len(set(df[feature]))))
sp.pprint(set(df[feature]))
def robust_scaler(Xtrain,ytrain,Xtest,ytest):
scale_X=RobustScaler(); scale_y=RobustScaler()
Xtrain=scale_X.fit_transform(Xtrain)
Xtest=scale_X.transform(Xtest)
ytrain=scale_y.fit_transform(ytrain.reshape(-1,1))
ytest=scale_y.transform(ytest.reshape(-1,1))
return Xtrain,ytrain,Xtest,ytest
def regression(regressor,X_train,X_test,y_train):
regressor.fit(X_train,y_train)
return regressor.predict(X_train),regressor.predict(X_test)
def scores(title,y_train,y_test,y_train_reg,y_test_reg):
separator='-_-'*10; sp.pprint(title); print('<_>'*20)
print("EV score. Train: "+\
str(explained_variance_score(y_train,y_train_reg)))
print("EV score. Test: "+\
str(explained_variance_score(y_test,y_test_reg)))
print(separator)
print("R2 score. Train: "+\
str(r2_score(y_train,y_train_reg)))
print("R2 score. Test: "+\
str(r2_score(y_test,y_test_reg)))
print(separator)
print("MSE score. Train: "+\
str(mean_squared_error(y_train,y_train_reg)))
print("MSE score. Test: "+\
str(mean_squared_error(y_test,y_test_reg)))
print(separator)
print("MAE score. Train: "+\
str(mean_absolute_error(y_train,y_train_reg)))
print("MAE score. Test: "+\
str(mean_absolute_error(y_test,y_test_reg)))
print(separator)
print("MdAE score. Train: "+\
str(median_absolute_error(y_train,y_train_reg)))
print("MdAE score. Test: "+\
str(median_absolute_error(y_test,y_test_reg)))
def display_importance(regressor,X,y,title,n):
importances=regressor.fit(X,y).feature_importances_
indices=np.argsort(importances)[::-int(1)][:int(n)]
pl.figure(figsize=(11,4))
pl.xlabel("Feature Index"); pl.ylabel("Feature Importance")
pl.bar(range(n),importances[indices],
color="forestgreen",align="center",alpha=.5)
pl.xticks(range(n),indices); pl.title(title); pl.show()
def history_plot(fit_history):
keys=list(fit_history.history.keys())[0:4]
pl.figure(figsize=(11,9)); pl.subplot(211)
pl.plot(fit_history.history[keys[0]],
color='#31c831',label='train')
pl.plot(fit_history.history[keys[2]],
color='forestgreen',label='test')
pl.xlabel("Epochs"); pl.ylabel("Loss")
pl.legend(); pl.title('Loss Function'); pl.subplot(212)
pl.plot(fit_history.history[keys[1]],
color='#31c831',label='train')
pl.plot(fit_history.history[keys[3]],
color='forestgreen',label='test')
pl.xlabel("Epochs"); pl.ylabel("MAE"); pl.legend()
pl.title('Mean Absolute Error'); pl.show()
</script></div><br/>
<h2>Capstone Proposal Overview</h2>
In this capstone project proposal, the goals are stated to leverage what we've learned throughout the Nanodegree Program for solving a problem of our choice by applying machine learning techniques.<br/>
A project proposal encompasses seven key points.<br/>
<b>project's domain background</b>: the field of research where the project is derived;<br/>
<b>problem statement</b>: a problem being investigated for which a solution will be defined;<br/>
<b>datasets and inputs</b>: data or inputs being used for the problem;<br/>
<b>solution statement</b>: the solution proposed for the problem given;<br/>
<b>benchmark model</b>: some simple or historical model or result to compare the defined solution to;<br/>
<b>set of evaluation metrics</b>: functional representations for how the solution can be measured;<br/>
<b>outline of the project design</b>: how the solution will be developed and results obtained.<br/>
The full project report about results will be completed and published as well.
<h2>Domain Background</h2>
Housing costs demand a significant investment from both consumers and developers. <br/>
And when it comes to planning a budget—whether personal or corporate—the last thing anyone needs is uncertainty about one of their budgets expenses. <br/>
Sberbank, Russia’s oldest and largest bank, helps their customers by making predictions about reality prices so renters, developers, <br/>and lenders are more confident when they sign a lease or purchase a building.<br/>
Although the housing market is relatively stable in Russia, the country’s volatile economy makes forecasting prices as a function of apartment characteristics a unique challenge. <br/>
Complex interactions between housing features such as a number of bedrooms and location are enough to make pricing predictions complicated. <br/>
Adding an unstable economy to the mix means Sberbank and their customers need more than simple regression models in their arsenal.
<h2>Problem Statement</h2>
Sberbank is challenging programmers to develop algorithms which use a broad spectrum of features to predict real prices. <br/>Algorithm applications rely on a rich dataset that includes housing data and macroeconomic patterns.<br/>
An accurate forecasting model will allow Sberbank to provide more certainty to their customers in an uncertain economy.<br/>
My choice of the solution in this situation is to select the most correlated indicators with the target variable and <br/>
apply ensemble algorithms that have repeatedly shown successful results in the study of price trends in real estate. <br/>
Boosting and bagging methods combine several models at once in order to improve the prediction accuracy on learning problems with a numerical target variable.<br/>
Then I'm going to explore different types of neural networks for regression predictions and try to achieve the same with ensemble methods level of model perfomance.
<h2>Datasets and Inputs</h2>
The basis for the investigation is a large number of economic indicators for pricing and prices themselves (<i>train.csv</i> and <i>test.csv</i>).<br/>
Macroeconomic variables are collected in a separate file for transaction dates (<i>macro.csv</i>). <br/>
In addition, the detailed description of variables is provided (<i>data_dictionary.txt</i>).<br/>
For practical reasons, I have not analyzed all the data and have chosen the following independent variables:<br/>
- the dollar rate, which traditionally affects the Russian real estate market;<br/>
- the distance in km from the Kremlin (the closer to the center of the city, the more expensive);<br/>
- indicators characterizing the availability of urban infrastructure nearby (schools, medical and sports centers, supermarkets, etc.);<br/>
- indicators of a particular living space (number of rooms, floor, etc.);<br/>
- proximity to transport nodes (for example, to the metro);<br/>
- indicators of population density and employment in the region of housing accommodation.<br/>
All these economic indicators have a strong influence on price formation and can be used as a basic set for regression analysis.<br/>
Examples of <b>numerical</b> variables: the distance to the metro, the distance to the school, the dollar rate at the transaction moment, the area of the living space. <br/>
Examples of <b>categorical</b> variables: neighborhoods, the nearest metro station, the number of rooms.<br/>
The goal of the project is to predict the price of housing using the chosen set of numerical and categorical variables.<br/>
The predicted target isn't discrete, for the training set all the values of this dependent variable are given, and therefore it's necessary to apply regression algorithms of supervised learning.
<h3>Data Description</h3>
<div class="sage"><script type="text/x-sage">
%%html
<div id="data">
<iframe src="data_dictionary.txt" height="200" width="98%">
</iframe></div>
</script></div><br/>
<h3>Load and Display the Data</h3>
<div class="linked"><script type="text/x-sage">
path='https://raw.githubusercontent.com/OlgaBelitskaya/'+\
'machine_learning_engineer_nd009/'+\
'master/Machine_Learning_Engineer_ND_P6/'
macro=pd.read_csv(path+'macro.csv')
train=pd.read_csv(path+'train.csv')
test=pd.read_csv(path+'test.csv')
n1,n2,n3,n4=int(100),int(110),int(1),int(15)
macro[n1:n2].T[n3:n4]
</script></div><br/>
<div class="linked"><script type="text/x-sage">
train[n1:n2].T[n3:n4]
</script></div><br/>
<h2>Solution Statement</h2>
<h3>Selection of Features</h3>
<p>Create lists of the features</p>
<div class="linked"><script type="text/x-sage">
X_list_num=['timestamp','full_sq','floor','max_floor','num_room','area_m',
'kremlin_km','big_road2_km','big_road1_km','workplaces_km',
'stadium_km','swim_pool_km','fitness_km','detention_facility_km',
'cemetery_km','radiation_km','oil_chemistry_km',
'theater_km','exhibition_km','museum_km','park_km',
'public_healthcare_km','metro_min_walk','metro_km_avto',
'bus_terminal_avto_km','public_transport_station_min_walk',
'railroad_station_walk_min','railroad_station_avto_km',
'kindergarten_km','school_km','preschool_km','university_km',
'additional_education_km','shopping_centers_km',
'big_market_km','ekder_all','work_all','young_all']
X_list_cat=['sub_area','ID_metro','office_raion','sport_objects_raion',
'raion_popul','healthcare_centers_raion',
'school_education_centers_raion',
'preschool_education_centers_raion']
features_train=train[X_list_num]
features_test=test[X_list_num]
target_train=train['price_doc']
</script></div><br/>
<p>Create the distribution plot for the target</p>
<div class="linked"><script type="text/x-sage">
f,(ax1,ax2)=pl.subplots(ncols=2,figsize=(11,5))
sn.distplot(target_train,bins=200,color='#31c831',ax=ax1)
ax1.set_xlabel("Prices"); ax1.set_ylabel("Distribution")
sn.distplot(np.log(target_train),bins=200,color='#31c831',ax=ax2)
ax2.set_xlabel("Log(Prices)")
ax2.set_ylabel("Distribution")
pl.suptitle('Sberbank Russian Housing Data'); pl.show()
</script></div><br/>
<p>Create the table of descriptive statistics</p>
<div class="linked"><script type="text/x-sage">
print("Sberbank Russian Housing Dataset Statistics: \n")
print("Number of houses = ",len(target_train))
print("Number of features = ",len(list(features_train.keys())))
print("Minimum house price = ",np.min(target_train))
print("Maximum house price = ",np.max(target_train))
print("Mean house price = ", "%.2f"%np.mean(target_train))
print("Median house price = ", "%.2f"%np.median(target_train))
print("Standard deviation of house prices =","%.2f"%np.std(target_train))
</script></div><br/>
<h3>Fill in Missing Values</h3>
<div class="linked"><script type="text/x-sage">
pd.DataFrame(train[X_list_num].isnull()\
.sum()[train[X_list_num].isnull().sum()!=0],columns=['nan'])
</script></div><br/>
<div class="linked"><script type="text/x-sage">
pd.DataFrame(test[X_list_num].isnull()\
.sum()[test[X_list_num].isnull().sum()!=0],columns=['nan'])
</script></div><br/>
<div class="linked"><script type="text/x-sage">
df_train=pd.DataFrame(train,columns=X_list_num)
df_train_cat=pd.DataFrame(train,columns=X_list_num+X_list_cat)
df_test=pd.DataFrame(test,columns=X_list_num)
df_test_cat=pd.DataFrame(test,columns=X_list_num+X_list_cat)
df_train['prices']=target_train
df_train_cat['prices']=target_train
df_train=df_train.dropna(subset=['num_room'])
df_train_cat=df_train_cat.dropna(subset=['num_room'])
for el in ['metro_min_walk','railroad_station_walk_min','floor']:
for df in [df_train,df_train_cat,df_test,df_test_cat]:
df[el]=df[el].interpolate(method='linear')
len(df_train),len(df_test)
</script></div><br/>
<h3>Categorical and Macro Features</h3>
<h4>Add One Macro Feature</h4>
<div class="linked"><script type="text/x-sage">
usdrub_pairs=dict(zip(list(macro['timestamp']),list(macro['usdrub'])))
# salary_pairs=dict(zip(list(macro['timestamp']),list(macro['salary'])))
for df in [df_train,df_train_cat,df_test,df_test_cat]:
df['timestamp'].replace(usdrub_pairs,inplace=True)
df.rename(columns={'timestamp':'usdrub'},inplace=True)
</script></div><br/>
<h4>Explore numbers of categories and values for categorical features</h4>
<div class="linked"><script type="text/x-sage">
for feature in X_list_cat:
display_cat(df_train_cat,feature)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
for feature in X_list_cat:
display_cat(df_test_cat,feature)
</script></div><br/>
<p>Find the missing category in the testing set</p>
<div class="linked"><script type="text/x-sage">
for feature in X_list_cat:
for element in list(set(df_test_cat[feature])):
if element not in list(set(df_train_cat[feature])):
print (feature,element)
</script></div><br/>
<p>Replace values of 'ID_metro' and other categorical features by discrete numbers</p>
<div class="linked"><script type="text/x-sage">
ID_metro_cat=pd.factorize(df_train_cat['ID_metro'])
ID_metro_pairs=dict(zip(list(ID_metro_cat[1]),list(set(ID_metro_cat[0]))))
ID_metro_pairs[224]=219
df_train_cat['ID_metro']=ID_metro_cat[0]
df_test_cat['ID_metro'].replace(ID_metro_pairs,inplace=True)
for feature in X_list_cat:
if feature!='ID_metro':
feature_cat=pd.factorize(df_train_cat[feature])
feature_pairs=dict(zip(list(feature_cat[1]),list(set(feature_cat[0]))))
df_train_cat[feature]=feature_cat[0]
df_test_cat[feature].replace(feature_pairs,inplace=True)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
for feature in X_list_cat:
display_cat(df_train_cat,feature)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
for feature in X_list_cat:
display_cat(df_test_cat,feature)
</script></div><br/>
<p>Apply one hot encoding</p>
<div class="linked"><script type="text/x-sage">
encode=OneHotEncoder(sparse=False)
df_train_cat1=df_train_cat.copy()
for column in X_list_cat:
encode.fit(df_train_cat[[column]])
transform=encode.transform(df_train_cat[[column]])
transform=pd.DataFrame(transform,columns=[(column+"_"+str(i))
for i in df_train_cat[column].value_counts().index])
transform=transform.set_index(df_train_cat.index.values)
df_train_cat1=pd.concat([df_train_cat1,transform],axis=int(1))
df_train_cat1=df_train_cat1.drop(column,1)
encode=OneHotEncoder(sparse=False); df_test_cat1=df_test_cat.copy()
for column in X_list_cat:
encode.fit(df_test_cat[[column]])
transform=encode.transform(df_test_cat[[column]])
transform=pd.DataFrame(transform,columns=[(column+"_"+str(i))
for i in df_test_cat[column].value_counts().index])
transform=transform.set_index(df_test_cat.index.values)
df_test_cat1=pd.concat([df_test_cat1,transform],axis=int(1))
df_test_cat1=df_test_cat1.drop(column,1)
# checking
n1,n2,n3=int(625),int(638),int(3)
df_train_cat1.iloc[:,n1:n2][:n3].values,\
df_train_cat['preschool_education_centers_raion'][:n3]
</script></div><br/>
<h4>Add Missing Columns with Zero Values</h4>
<div class="linked"><script type="text/x-sage">
print('Shape of the train data frame:'+str(df_train_cat1.shape))
print('Shape of the test data frame:'+str(df_test_cat1.shape))
print("Features in the train data, but not in the test data:")
for element in list(df_train_cat1):
if element not in list(df_test_cat1):
print(element)
print("Features in the test data, but not in the train data:")
for element in list(df_test_cat1):
if element not in list(df_train_cat1):
print(element)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
for column in ['sub_area_136','ID_metro_188','ID_metro_205',
'ID_metro_216','ID_metro_214',
'ID_metro_183','ID_metro_179','ID_metro_153',
'ID_metro_217','raion_popul_136']:
df_test_cat1[column]=0
df_train_cat1['ID_metro_219']=0
print('Columns with zero values were added.\n')
print('Shape of the train data frame:'+str(df_train_cat1.shape))
print('Shape of the test data frame:'+str(df_test_cat1.shape))
</script></div><br/>
<h3>Display Correlation</h3>
<div class="linked"><script type="text/x-sage">
corr_with_prices=df_train.corr(method='pearson')\
.iloc[-int(1)][:-int(1)]
pd.DataFrame(corr_with_prices[abs(corr_with_prices).argsort()[::-int(1)]])
</script></div><br/>
<div class="linked"><script type="text/x-sage">
features_list=corr_with_prices[abs(corr_with_prices)\
.argsort()[::-int(1)]][:int(10)].index.values.tolist()
print('The most correlated with prices:\n'); features_list
</script></div><br/>
<div class="linked"><script type="text/x-sage">
pl.figure(figsize=(11,11)); cols=df_train.corr().columns.values
sn.heatmap(df_train.corr(),cmap=pl.cm.Greens,
xticklabels=cols,yticklabels=cols)
pl.title("Correlation Matrix",fontsize=15)
pl.xticks(fontsize=5); pl.yticks(fontsize=6); pl.show()
</script></div><br/>
<h3>Scale, Shuffle and Split the Data</h3>
<div class="linked"><script type="text/x-sage">
target_train,features_train=\
df_train['prices'].values,df_train.drop('prices',1).values
features_train_cat,features_train_cat_enc=\
df_train_cat.drop('prices',1).values,\
df_train_cat1.drop('prices',1).values
features_test,features_test_cat,features_test_cat_enc=\
df_test.values,df_test_cat.values,df_test_cat1.values
sp.pprint('Numeric Features')
X_train,X_test,y_train,y_test=\
train_test_split(features_train,target_train,test_size=.2,random_state=1)
X_train.shape,X_test.shape,y_train.shape,y_test.shape
</script></div><br/>
<div class="linked"><script type="text/x-sage">
sp.pprint('Numeric and Categorical Features')
X_train_cat,X_test_cat,y_train_cat,y_test_cat=\
train_test_split(features_train_cat,target_train,test_size=.2,random_state=1)
X_train_cat.shape,X_test_cat.shape,y_train_cat.shape,y_test_cat.shape
</script></div><br/>
<div class="linked"><script type="text/x-sage">
sp.pprint('Numeric and Encoded Categorical Features')
X_train_cat_enc,X_test_cat_enc,y_train_cat_enc,y_test_cat_enc=\
train_test_split(features_train_cat_enc,target_train,test_size=.2,random_state=1)
X_train_cat_enc.shape,X_test_cat_enc.shape,\
y_train_cat_enc.shape,y_test_cat_enc.shape
</script></div><br/>
<div class="linked"><script type="text/x-sage">
#for [Xtrain,ytrain,Xtest,ytest] in \
#[[X_train,y_train,X_test,y_test],
# [X_train_cat,y_train_cat,X_test_cat,y_test_cat],
# [X_train_cat_enc,y_train_cat_enc,X_test_cat_enc,y_test_cat_enc]]:
# Xtrain,ytrain,Xtest,ytest=robust_scaler(Xtrain,ytrain,Xtest,ytest)
del macro,train,test,df_train,df_train_cat,df_train_cat1
del df_test,df_test_cat,df_test_cat1
del target_train,features_train,features_train_cat,features_train_cat_enc
del features_test,features_test_cat,features_test_cat_enc
</script></div><br/>
<h2>Benchmark Models</h2>
To compare the prediction quality, I chose the most effective (for financial indicators) regression ensemble algorithms and neural networks (for example, multilayer perceptrons).<br/>
In addition, I was wondering what the highest accuracy rate will be achieved by each of the presented algorithms
and whether the predicted trends of price change for all used types of techniques will coincide.
<h3>Regressors. Scikit-Learn</h3>
<div class="linked"><script type="text/x-sage">
reg=GradientBoostingRegressor(max_depth=4,n_estimators=38*4)
y_train_gbr,y_test_gbr=\
regression(reg,X_train,X_test,y_train)
scores('GradientBoostingRegressor. Numeric Features',
y_train,y_test,y_train_gbr,y_test_gbr)
ti="Importance of Numeric Features. Gradient Boosting Regressor"
display_importance(reg,X_train,y_train,ti,38)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
reg_cat=GradientBoostingRegressor(max_depth=4,n_estimators=46)
y_train_cat_gbr,y_test_cat_gbr=\
regression(reg_cat,X_train_cat,X_test_cat,y_train_cat)
ti='GradientBoostingRegressor. Numeric and Categorical Features'
scores(ti,y_train_cat,y_test_cat,y_train_cat_gbr,y_test_cat_gbr)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
reg_cat_enc=GradientBoostingRegressor(max_depth=3,n_estimators=16)
y_train_cat_enc_gbr,y_test_cat_enc_gbr=\
regression(reg_cat_enc,X_train_cat_enc,X_test_cat_enc,y_train_cat_enc)
scores('GradientBoostingRegressor. Numeric and Encoded Categorical Features',
y_train_cat_enc,y_test_cat_enc,
y_train_cat_enc_gbr,y_test_cat_enc_gbr)
</script></div><br/>
<h3>MLP Regressors. Scikit-Learn</h3>
<h4>Regressors</h4>
<div class="linked"><script type="text/x-sage">
mlpr=MLPRegressor(hidden_layer_sizes=(32*8,),max_iter=15,
solver='adam',batch_size=14,
learning_rate='adaptive',verbose='True')
mlpr.fit(X_train,y_train)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
y_train_mlpr=mlpr.predict(X_train)
y_test_mlpr=mlpr.predict(X_test)
scores('MLP Regressor. Numeric Features',
y_train,y_test,y_train_mlpr,y_test_mlpr)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
mlpr_cat=MLPRegressor(hidden_layer_size=(32*8,),max_iter=10,
solver='adam',batch_size=14,
learning_rate='adaptive',verbose='True')
mlpr_cat.fit(X_train_cat,y_train_cat)
y_train_cat_mlpr=mlpr_cat.predict(X_train_cat)
y_test_cat_mlpr=mlpr_cat.predict(X_test_cat)
scores('MLP Regressor. Numeric and Categorical Features',
y_train_cat,y_test_cat,y_train_cat_mlpr,y_test_cat_mlpr)
</script></div><br/>
<div class="linked"><script type="text/x-sage">
#mlpr_cat_enc=MLPRegressor(hidden_layer_sizes=(32*8,),max_iter=10,
# solver='adam',batch_size=14,
# learning_rate='adaptive',verbose='True')
#mlpr_cat_enc.fit(X_train_cat_enc,y_train_cat_enc)
#y_train_cat_enc_mlpr=mlpr_cat_enc.predict(X_train_cat_enc)
#y_test_cat_enc_mlpr=mlpr_cat_enc.predict(X_test_cat_enc)
#scores('MLP Regressor. Numeric and Categorical Encoded Features',
# y_train_cat_enc,y_test_cat_enc,
# y_train_cat_enc_mlpr,y_test_cat_enc_mlpr)
</script></div><br/>
<h3>Display Predictions</h3>
<div class="linked"><script type="text/x-sage">
n=int(50); pl.figure(figsize=(11,5))
pl.plot(y_test[1:n],color='black',label='Real Data')
pl.plot(y_test_gbr[1:n],label='Gradient Boosting')
pl.plot(y_test_mlpr[1:n],label='MLP')
ti="PCA. Numeric Features. Regressor Predictions vs Real Data"
pl.legend(); pl.title(ti); pl.show()
</script></div><br/>
<div class="linked"><script type="text/x-sage">
n=int(50); pl.figure(figsize=(11,5))
pl.plot(y_test_cat[1:n],color='black',label='Real Data')
pl.plot(y_test_cat_gbr[1:n],label='Gradient Boosting')
pl.plot(y_test_cat_mlpr[1:n],label='MLP')
ti="PCA. Numeric and Categorical Features. Regressor Predictions vs Real Data"
pl.legend(); pl.title(ti); pl.show()
</script></div><br/>
<h3>Additional Code Cell</h3>
<div class="linked"><script type="text/x-sage">
</script></div><br/>
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