It is now clear that without exploring feature selection and engineering the classifiers cannot process all the individuals as there is just too much noise. Therefor I am going to experiment with 2 approaches.
This notebook will outline approach 1.
I propose 2 segregations
Also I feel running a full gamut of classifiers is time consuming. Therefore I am limiting it to
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# Standard
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Dimensionality reduction and Clustering
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn import manifold, datasets
from itertools import cycle
# Plotting tools and classifiers
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
from sklearn import cross_validation
from sklearn.cross_validation import LeaveOneOut
# Let's read the data in and clean it
def get_NaNs(df):
columns = list(df.columns.get_values())
row_metrics = df.isnull().sum(axis=1)
rows_with_na = []
for i, x in enumerate(row_metrics):
if x > 0: rows_with_na.append(i)
return rows_with_na
def remove_NaNs(df):
rows_with_na = get_NaNs(df)
cleansed_df = df.drop(df.index[rows_with_na], inplace=False)
return cleansed_df
initial_data = pd.DataFrame.from_csv('Data_Adults_1_reduced.csv')
cleansed_df = remove_NaNs(initial_data)
# Let's also get rid of nominal data
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
X = cleansed_df.select_dtypes(include=numerics)
print X.shape
The change from last week was that we also threw out
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# Let's now clean columns getting rid of certain columns that might not be important to our analysis
cols2drop = ['GROUP_ID', 'doa', 'Baseline_header_id', 'Concentration_header_id',
'Baseline_Reading_id', 'Concentration_Reading_id']
X = X.drop(cols2drop, axis=1, inplace=False)
print X.shape
# For our studies children skew the data, it would be cleaner to just analyse adults
X = X.loc[X['Age'] >= 18]
Y = X.loc[X['race_id'] == 1]
X = X.loc[X['Gender_id'] == 1]
print X.shape
print Y.shape
we've now dropped the last of the discrete numerical inexplicable data, and removed children from the mix
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# Let's extract ADHd and Bipolar patients (mutually exclusive)
ADHD_men = X.loc[X['ADHD'] == 1]
ADHD_men = ADHD_men.loc[ADHD_men['Bipolar'] == 0]
BP_men = X.loc[X['Bipolar'] == 1]
BP_men = BP_men.loc[BP_men['ADHD'] == 0]
ADHD_cauc = Y.loc[Y['ADHD'] == 1]
ADHD_cauc = ADHD_cauc.loc[ADHD_cauc['Bipolar'] == 0]
BP_cauc = Y.loc[Y['Bipolar'] == 1]
BP_cauc = BP_cauc.loc[BP_cauc['ADHD'] == 0]
print ADHD_men.shape
print BP_men.shape
print ADHD_cauc.shape
print BP_cauc.shape
# Keeping a backup of the data frame object because numpy arrays don't play well with certain scikit functions
ADHD_men = pd.DataFrame(ADHD_men.drop(['Patient_ID', 'Gender_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))
BP_men = pd.DataFrame(BP_men.drop(['Patient_ID', 'Gender_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))
ADHD_cauc = pd.DataFrame(ADHD_cauc.drop(['Patient_ID', 'race_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))
BP_cauc = pd.DataFrame(BP_cauc.drop(['Patient_ID', 'race_id', 'ADHD', 'Bipolar'], axis = 1, inplace = False))
we see here that there 1383 people who have ADHD but are not Bipolar and 440 people who are Bipolar but do not have ADHD
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combined1 = pd.concat([ADHD_men, BP_men])
combined2 = pd.concat([ADHD_cauc, BP_cauc])
print combined1.shape
print combined2.shape
combined1 = preprocessing.scale(combined1)
combined2 = preprocessing.scale(combined2)
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combined1 = manifold.Isomap(20, 20).fit_transform(combined1)
ADHD_men_iso = combined1[:1056]
BP_men_iso = combined1[1056:]
combined2 = manifold.Isomap(20, 20).fit_transform(combined2)
ADHD_cauc_iso = combined2[:1110]
BP_cauc_iso = combined2[1110:]
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data1 = pd.concat([pd.DataFrame(ADHD_men_iso), pd.DataFrame(BP_men_iso)])
data2 = pd.concat([pd.DataFrame(ADHD_cauc_iso), pd.DataFrame(BP_cauc_iso)])
print data1.shape
print data2.shape
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kmeans = KMeans(n_clusters=2)
kmeans.fit(data1.get_values())
labels1 = kmeans.labels_
centroids1 = kmeans.cluster_centers_
print('Estimated number of clusters: %d' % len(centroids1))
for label in [0, 1]:
ds = data1.get_values()[np.where(labels1 == label)]
plt.plot(ds[:,0], ds[:,1], '.')
lines = plt.plot(centroids1[label,0], centroids1[label,1], 'o')
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kmeans = KMeans(n_clusters=2)
kmeans.fit(data2.get_values())
labels2 = kmeans.labels_
centroids2 = kmeans.cluster_centers_
print('Estimated number of clusters: %d' % len(centroids2))
for label in [0, 1]:
ds2 = data2.get_values()[np.where(labels2 == label)]
plt.plot(ds2[:,0], ds2[:,1], '.')
lines = plt.plot(centroids2[label,0], centroids2[label,1], 'o')
As is evident from the above 2 experiments, no clear clustering is apparent.But there is some significant overlap and there 2 clear groups
Let's experiment with a bunch of classifiers
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ADHD_men_iso = pd.DataFrame(ADHD_men_iso)
BP_men_iso = pd.DataFrame(BP_men_iso)
ADHD_cauc_iso = pd.DataFrame(ADHD_cauc_iso)
BP_cauc_iso = pd.DataFrame(BP_cauc_iso)
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BP_men_iso['ADHD-Bipolar'] = 0
ADHD_men_iso['ADHD-Bipolar'] = 1
BP_cauc_iso['ADHD-Bipolar'] = 0
ADHD_cauc_iso['ADHD-Bipolar'] = 1
data1 = pd.concat([ADHD_men_iso, BP_men_iso])
data2 = pd.concat([ADHD_cauc_iso, BP_cauc_iso])
class_labels1 = data1['ADHD-Bipolar']
class_labels2 = data2['ADHD-Bipolar']
data1 = data1.drop(['ADHD-Bipolar'], axis = 1, inplace = False)
data2 = data2.drop(['ADHD-Bipolar'], axis = 1, inplace = False)
data1 = data1.get_values()
data2 = data2.get_values()
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# Leave one Out cross validation
def leave_one_out(classifier, values, labels):
leave_one_out_validator = LeaveOneOut(len(values))
classifier_metrics = cross_validation.cross_val_score(classifier, values, labels, cv=leave_one_out_validator)
accuracy = classifier_metrics.mean()
deviation = classifier_metrics.std()
return accuracy, deviation
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rf = RandomForestClassifier(n_estimators = 22)
qda = QDA()
lda = LDA()
gnb = GaussianNB()
classifier_accuracy_list = []
classifiers = [(rf, "Random Forest"), (lda, "LDA"), (qda, "QDA"), (gnb, "Gaussian NB")]
for classifier, name in classifiers:
accuracy, deviation = leave_one_out(classifier, data1, class_labels1)
print '%s accuracy is %0.4f (+/- %0.3f)' % (name, accuracy, deviation)
classifier_accuracy_list.append((name, accuracy))
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for classifier, name in classifiers:
accuracy, deviation = leave_one_out(classifier, data2, class_labels2)
print '%s accuracy is %0.4f (+/- %0.3f)' % (name, accuracy, deviation)
classifier_accuracy_list.append((name, accuracy))
1) What did you separate your data into and how?
Ans: Male ADHD patients vs Male Bipolar Patients and Caucasian ADHD patients vs Caucasian Bipolar patients
2) How did you reduce dimensionality?
Ans: We used an isomap with 20 features
3) Was there any clustering apparent?
Ans: Not to me, and visually not so much
4) How did you classify and how did you validate?
Ans: I ran LDA, QDA, Random forest and Gaussian NB with leave one out cross validation
5) What were the results?
Ans: Male study
Random Forest accuracy is 0.9497 (+/- 0.218)
LDA accuracy is 0.9688 (+/- 0.174)
QDA accuracy is 0.9467 (+/- 0.225)
Gaussian NB accuracy is 0.9452 (+/- 0.228)
Race Study
Random Forest accuracy is 0.9393 (+/- 0.239)
LDA accuracy is 0.9574 (+/- 0.202)
QDA accuracy is 0.9505 (+/- 0.217)
Gaussian NB accuracy is 0.9442 (+/- 0.230)
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