Crazy

[naveenr]

Lasso regression

Lasso regression

import pandas as pd import numpy as np import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LassoLarsCV

Load the dataset

data = pd.read_csv("../machine learning/data/tree_addhealth.csv")

upper-case all DataFrame column names

data.columns = map(str.upper, data.columns)

Data Management

data_clean = data.dropna() recode1 = {1:1, 2:0} data_clean['MALE']= data_clean['BIO_SEX'].map(recode1)

select predictor variables and target variable as separate data sets  

predvar= data_clean[['MALE','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN', 'AGE','ALCEVR1','ALCPROBS1','MAREVER1','COCEVER1','INHEVER1','CIGAVAIL','DEP1', 'ESTEEM1','VIOL1','PASSIST','DEVIANT1','GPA1','EXPEL1','FAMCONCT','PARACTV', 'PARPRES']]

target = data_clean.SCHCONN1

standardize predictors to have mean=0 and sd=1

predictors=predvar.copy() from sklearn import preprocessing predictors['MALE']=preprocessing.scale(predictors['MALE'].astype('float64')) predictors['HISPANIC']=preprocessing.scale(predictors['HISPANIC'].astype('float64')) predictors['WHITE']=preprocessing.scale(predictors['WHITE'].astype('float64')) predictors['NAMERICAN']=preprocessing.scale(predictors['NAMERICAN'].astype('float64')) predictors['ASIAN']=preprocessing.scale(predictors['ASIAN'].astype('float64')) predictors['AGE']=preprocessing.scale(predictors['AGE'].astype('float64')) predictors['ALCEVR1']=preprocessing.scale(predictors['ALCEVR1'].astype('float64')) predictors['ALCPROBS1']=preprocessing.scale(predictors['ALCPROBS1'].astype('float64')) predictors['MAREVER1']=preprocessing.scale(predictors['MAREVER1'].astype('float64')) predictors['COCEVER1']=preprocessing.scale(predictors['COCEVER1'].astype('float64')) predictors['INHEVER1']=preprocessing.scale(predictors['INHEVER1'].astype('float64')) predictors['CIGAVAIL']=preprocessing.scale(predictors['CIGAVAIL'].astype('float64')) predictors['DEP1']=preprocessing.scale(predictors['DEP1'].astype('float64')) predictors['ESTEEM1']=preprocessing.scale(predictors['ESTEEM1'].astype('float64')) predictors['VIOL1']=preprocessing.scale(predictors['VIOL1'].astype('float64')) predictors['PASSIST']=preprocessing.scale(predictors['PASSIST'].astype('float64')) predictors['DEVIANT1']=preprocessing.scale(predictors['DEVIANT1'].astype('float64')) predictors['GPA1']=preprocessing.scale(predictors['GPA1'].astype('float64')) predictors['EXPEL1']=preprocessing.scale(predictors['EXPEL1'].astype('float64')) predictors['FAMCONCT']=preprocessing.scale(predictors['FAMCONCT'].astype('float64')) predictors['PARACTV']=preprocessing.scale(predictors['PARACTV'].astype('float64')) predictors['PARPRES']=preprocessing.scale(predictors['PARPRES'].astype('float64'))

split data into train and test sets

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, target,                                                              test_size=.3, random_state=123)

specify the lasso regression model

model=LassoLarsCV(cv=10, precompute=False).fit(pred_train,tar_train)

print variable names and regression coefficients

dict(zip(predictors.columns, model.coef_))

plot coefficient progression

m_log_alphas = -np.log10(model.alphas_) ax = plt.gca() plt.plot(m_log_alphas, model.coef_path_.T) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',            label='alpha CV') plt.ylabel('Regression Coefficients') plt.xlabel('-log(alpha)') plt.title('Regression Coefficients Progression for Lasso Paths')

plot mean square error for each fold

m_log_alphascv = -np.log10(model.cv_alphas_) plt.figure() plt.plot(m_log_alphascv, model.cv_mse_path_, ':') plt.plot(m_log_alphascv, model.cv_mse_path_.mean(axis=-1), 'k',         label='Average across the folds', linewidth=2) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',            label='alpha CV') plt.legend() plt.xlabel('-log(alpha)') plt.ylabel('Mean squared error') plt.title('Mean squared error on each fold')

MSE from training and test data

from sklearn.metrics import mean_squared_error train_error = mean_squared_error(tar_train, model.predict(pred_train)) test_error = mean_squared_error(tar_test, model.predict(pred_test)) print ('training data MSE') print(train_error) print ('test data MSE') print(test_error)

R-square from training and test data

rsquared_train=model.score(pred_train,tar_train) rsquared_test=model.score(pred_test,tar_test) print ('training data R-square') print(rsquared_train) print ('test data R-square') print(rsquared_test)

[naveenr]

K mean cluster

from pandas import Series, DataFrame import pandas as pd import numpy as np import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.cluster import KMeans

""" Data Management """ data = pd.read_csv("../machine learning/data/tree_addhealth.csv")

upper-case all DataFrame column names

data.columns = map(str.upper, data.columns)

Data Management

data_clean = data.dropna()

subset clustering variables

cluster=data_clean[['ALCEVR1','MAREVER1','ALCPROBS1','DEVIANT1','VIOL1', 'DEP1','ESTEEM1','SCHCONN1','PARACTV', 'PARPRES','FAMCONCT']] cluster.describe()

standardize clustering variables to have mean=0 and sd=1

clustervar=cluster.copy() clustervar['ALCEVR1']=preprocessing.scale(clustervar['ALCEVR1'].astype('float64')) clustervar['ALCPROBS1']=preprocessing.scale(clustervar['ALCPROBS1'].astype('float64')) clustervar['MAREVER1']=preprocessing.scale(clustervar['MAREVER1'].astype('float64')) clustervar['DEP1']=preprocessing.scale(clustervar['DEP1'].astype('float64')) clustervar['ESTEEM1']=preprocessing.scale(clustervar['ESTEEM1'].astype('float64')) clustervar['VIOL1']=preprocessing.scale(clustervar['VIOL1'].astype('float64')) clustervar['DEVIANT1']=preprocessing.scale(clustervar['DEVIANT1'].astype('float64')) clustervar['FAMCONCT']=preprocessing.scale(clustervar['FAMCONCT'].astype('float64')) clustervar['SCHCONN1']=preprocessing.scale(clustervar['SCHCONN1'].astype('float64')) clustervar['PARACTV']=preprocessing.scale(clustervar['PARACTV'].astype('float64')) clustervar['PARPRES']=preprocessing.scale(clustervar['PARPRES'].astype('float64'))

split data into train and test sets

clus_train, clus_test = train_test_split(clustervar, test_size=.3, random_state=123)

k-means cluster analysis for 1-9 clusters                                                          

from scipy.spatial.distance import cdist clusters=range(1,10) meandist=[]

for k in clusters:    model=KMeans(n_clusters=k)    model.fit(clus_train)    clusassign=model.predict(clus_train)    meandist.append(sum(np.min(cdist(clus_train, model.cluster_centers_, 'euclidean'), axis=1))    / clus_train.shape[0])

""" Plot average distance from observations from the cluster centroid to use the Elbow Method to identify number of clusters to choose """

plt.plot(clusters, meandist) plt.xlabel('Number of clusters') plt.ylabel('Average distance') plt.title('Selecting k with the Elbow Method')

Interpret 3 cluster solution

model3=KMeans(n_clusters=3) model3.fit(clus_train) clusassign=model3.predict(clus_train)

plot clusters

from sklearn.decomposition import PCA pca_2 = PCA(2) plot_columns = pca_2.fit_transform(clus_train) plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=model3.labels_) plt.xlabel('Canonical variable 1') plt.ylabel('Canonical variable 2') plt.title('Scatterplot of Canonical Variables for 3 Clusters') plt.show()

""" BEGIN multiple steps to merge cluster assignment with clustering variables to examine cluster variable means by cluster """

create a unique identifier variable from the index for the

cluster training data to merge with the cluster assignment variable

clus_train.reset_index(level=0, inplace=True)

create a list that has the new index variable

cluslist=list(clus_train['index'])

create a list of cluster assignments

labels=list(model3.labels_)

combine index variable list with cluster assignment list into a dictionary

newlist=dict(zip(cluslist, labels)) newlist

convert newlist dictionary to a dataframe

newclus=DataFrame.from_dict(newlist, orient='index') newclus

rename the cluster assignment column

newclus.columns = ['cluster']

now do the same for the cluster assignment variable

create a unique identifier variable from the index for the

cluster assignment dataframe

to merge with cluster training data

newclus.reset_index(level=0, inplace=True)

merge the cluster assignment dataframe with the cluster training variable dataframe

by the index variable

merged_train=pd.merge(clus_train, newclus, on='index') merged_train.head(n=100)

cluster frequencies

merged_train.cluster.value_counts()

""" END multiple steps to merge cluster assignment with clustering variables to examine cluster variable means by cluster """

FINALLY calculate clustering variable means by cluster

clustergrp = merged_train.groupby('cluster').mean() print ("Clustering variable means by cluster") print(clustergrp)

validate clusters in training data by examining cluster differences in GPA using ANOVA

first have to merge GPA with clustering variables and cluster assignment data

gpa_data=data_clean['GPA1']

split GPA data into train and test sets

gpa_train, gpa_test = train_test_split(gpa_data, test_size=.3, random_state=123) gpa_train1=pd.DataFrame(gpa_train) gpa_train1.reset_index(level=0, inplace=True) merged_train_all=pd.merge(gpa_train1, merged_train, on='index') sub1 = merged_train_all[['GPA1', 'cluster']].dropna()

import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi

gpamod = smf.ols(formula='GPA1 ~ C(cluster)', data=sub1).fit() print (gpamod.summary())

print ('means for GPA by cluster') m1= sub1.groupby('cluster').mean() print (m1)

print ('standard deviations for GPA by cluster') m2= sub1.groupby('cluster').std() print (m2)

mc1 = multi.MultiComparison(sub1['GPA1'], sub1['cluster']) res1 = mc1.tukeyhsd() print(res1.summary())

[naveenr]

Random forest

#Importing the required library

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics from sklearn import datasets from sklearn.ensemble import ExtraTreesClassifier

# Loading data and removing NA from the columns

AH_data = pd.read_csv("../machine learning/data/tree_addhealth.csv") data_clean = AH_data.dropna() data_clean

#Split into training and testing sets into 60 to 40 percent

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']] targets = data_clean.TREG1 pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4) pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#Build model on training data and having a random forest

from sklearn.ensemble import RandomForestClassifier classifier=RandomForestClassifier(n_estimators=25) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions) # fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train,tar_train) # display the relative importance of each attribute print(model.feature_importances_) """ Running a different number of trees and see the effect of that on the accuracy of the prediction """ trees=range(25) accuracy=np.zeros(25) for idx in range(len(trees)): classifier=RandomForestClassifier(n_estimators=idx + 1) classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) accuracy[idx]=sklearn.metrics.accuracy_score(tar_test, predictions) plt.cla() plt.plot(trees, accuracy)

#This below graph represents accuracy of all 25 trees of the random forest

[naveenr]

Decision tree python program.

#Importing all the required modules

from pandas import Series, DataFrame import pandas as pd import numpy as np import os import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import classification_report import sklearn.metrics

# Reading the csv file and assigning it to variable AH_data

AH_data = pd.read_csv("../machine learning/data/tree_addhealth.csv") AH_data

#Removing NA in the data

data_clean = AH_data.dropna()

data_clean.dtypes data_clean.describe()

training and testing sets

predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN', 'age','ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1', 'ESTEEM1','VIOL1','PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV', 'PARPRES']]

targets = data_clean.TREG1

#assigning and knowing the shape of each varible

, pred_test, tar_train, tar_test  =   train_test_split(predictors, targets, test_size=.4) pred_train.shape pred_test.shape tar_train.shape tar_test.shape

#building model on training set

classifier=DecisionTreeClassifier() classifier=classifier.fit(pred_train,tar_train) predictions=classifier.predict(pred_test) sklearn.metrics.confusion_matrix(tar_test,predictions) sklearn.metrics.accuracy_score(tar_test, predictions)

#Displaying the decision tree

from sklearn import tree from io import StringIO from IPython.display import Image out = StringIO() tree.export_graphviz(classifier, out_file=out) import pydotplus graph=pydotplus.graph_from_dot_data(out.getvalue()) Image(graph.create_png())

this below tree is a small section of decision tree