def fit(self, hyperparameters, X, y):
# PCA only works if the n_components is less than the min of the # of samples and # of columns
if hyperparameters['n_components'] < np.min(X.shape):
self.pca = sklearnPCA(**hyperparameters).fit(X)
else:
# Select the fewest number of features in all other cases
self.pca = sklearnPCA(**self.grid[0]).fit(X)
def get_pca():
pca_matrix=[]
error=0
filepath=session['filepath']
filetype=session['filetype']
labeltype=session['label']
request_data = json.loads(request.data)
#print "request data in /pca:"
#print request_data
ordering = request_data["order"]
distance_type=request_data["distance_type"]
dimension_factor=request_data["dimension_factor"]
pca_dimension_count=request_data["pca_dimension_count"]
order=ex.parse_order(ordering)
order=list(int(k) for k in order)
alpha=ex.readFromFile(filepath, filetype, labeltype,distance_type,dimension_factor)
dataset=np.array(alpha)
for orderno in order:
pca_matrix.append(dataset[orderno])
data_columns=len(pca_matrix[0])
if (data_columns >= pca_dimension_count):
sklearn_pca = sklearnPCA(n_components=pca_dimension_count)
Y_sklearn = sklearn_pca.fit_transform(pca_matrix)
final_pca_values=np.transpose(Y_sklearn)
final_pca_values=list(list(float(f) for f in d) for d in final_pca_values)
elif(data_columns == 2):
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(pca_matrix)
final_pca_values=np.transpose(Y_sklearn)
final_pca_values=list(list(float(f) for f in d) for d in final_pca_values)
else:
error=1
final_pca_values=[]
#print "final pca values:"
#print np.array(final_pca_values)
filePath=os.path.join(app.config['UPLOAD_FOLDER'], "results.txt")
f = open(filePath, "a")
f.write("PCA matrix for the ordering"+str(order)+"for "+str(len(final_pca_values))+"principal components:\n")
f.write(str(np.transpose(final_pca_values))+"\n\n")
f.close()
response_data={}
response_data["pca_values"]=final_pca_values
response_data["error_value"]=error
return json.dumps(response_data)
def pca_step_na(trans_std,promo_std):
from sklearn.decomposition import PCA as sklearnPCA
trans_pca = sklearnPCA(n_components=8)
trans_new = trans_pca.fit_transform(trans_std)
# promo PCA
promo_pca = sklearnPCA(n_components=12)
promo_new = promo_pca.fit_transform(promo_std)
pca_dict = {"trans":trans_pca,"promo":promo_pca}
return trans_new,promo_new,pca_dict
def pca_step(trans_std,food_std,promo_std):
from sklearn.decomposition import PCA as sklearnPCA
trans_pca = sklearnPCA(n_components=9)
trans_new = trans_pca.fit_transform(trans_std)
#food pca
food_pca = sklearnPCA(n_components=24)
food_new = food_pca.fit_transform(food_std)
# promo PCA
promo_pca = sklearnPCA(n_components=13)
promo_new = promo_pca.fit_transform(promo_std)
pca_dict = {"trans":trans_pca,"food":food_pca,"promo":promo_pca}
return trans_new,food_new,promo_new,pca_dict
def Linear_PCA(HE_MI_train_test, numdim=2):
'''
개요
- PCA: EIG value 순서대로 Linear Transform을 하여 그 순서대로 sorting
INPUT
- numdim : Dimension
OUTPUT
- 1. sklearn_HE_train_fit : numdim * numHEtrain
- 2. sklearn_MI_train_fit : numdim * numMItrain
- 3. sklearn_HE_test_fit : numdim * numHEtest
- 4. sklearn_MI_test_fit : numdim * numMItest
'''
MyDataSet = HE_MI_train_test
my_HEtraining = MyDataSet[0]
my_MItraining = MyDataSet[1]
my_HEtest = MyDataSet[2]
my_MItest = MyDataSet[3]
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=numdim)
sklearn_HE_train_fit = sklearn_pca.fit_transform(my_HEtraining)
sklearn_MI_train_fit = sklearn_pca.fit_transform(my_MItraining)
sklearn_HE_test_fit = sklearn_pca.fit_transform(my_HEtest)
sklearn_MI_test_fit = sklearn_pca.fit_transform(my_MItest)
return [sklearn_HE_train_fit, sklearn_MI_train_fit, sklearn_HE_test_fit, sklearn_MI_test_fit]
def plotPCA(labels, data, inputFile, outputFile, store=False):
sklearn_pca = sklearnPCA(n_components=2)
sklearn_pca.fit(data)
newData = sklearn_pca.transform(data)
xval = newData[:, 0]
yval = newData[:, 1]
lbls = set(labels)
#(predicted_labels)
fig1 = plt.figure(1)
#print(lbls)
for lbl in lbls:
#cond = predicted_labels == lbl
cond = [i for i, x in enumerate(labels) if x == lbl]
plt.plot(xval[cond],
yval[cond],
linestyle='none',
marker='o',
label=lbl,
markersize=3)
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(numpoints=1, loc=0, fontsize='x-small')
plt.subplots_adjust(bottom=.20, left=.20)
plt.grid()
fig1.suptitle("PCA plot for DBSCAN in " + inputFile.split("/")[-1],
fontsize=20)
if store:
fig1.savefig("_".join(
[outputFile, inputFile.split("/")[-1].split(".")[0]]) + ".png")
else:
plt.show()
def create_pca(self, input_tsv_file_for_pca, output_html):
df = pd.read_csv(filepath_or_buffer=input_tsv_file_for_pca,
header=0,
sep='\t')
X = df.ix[:, 1:24].values
y = df.ix[:, 23].values
#WARNING OCCURANCE
standardised_X = StandardScaler().fit_transform(X)
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(standardised_X)
traces = []
factor_group = df['Group'].unique()
print "The factors (groups) found: ", factor_group
for name in factor_group:
trace = Scatter(x=Y_sklearn[y == name, 0],
y=Y_sklearn[y == name, 1],
mode='markers',
name=name,
marker=Marker(
size=12,
line=Line(color='rgba(217, 217, 217, 0.14)',
width=0.5),
opacity=0.8))
traces.append(trace)
data = Data(traces)
layout = Layout(xaxis=XAxis(title='PC1', showline=False),
yaxis=YAxis(title='PC2', showline=False))
fig = Figure(data=data, layout=layout)
plot(fig, show_link=False, filename=output_html, auto_open=False)
def reduceDataset(self,nr=3,method='PCA'):
'''It reduces the dimensionality of a given dataset using different techniques provided by Sklearn library
Methods available:
'PCA'
'FactorAnalysis'
'KPCArbf','KPCApoly'
'KPCAcosine','KPCAsigmoid'
'IPCA'
'FastICADeflation'
'FastICAParallel'
'Isomap'
'LLE'
'LLEmodified'
'LLEltsa'
'''
dataset=self.ModelInputs['Dataset']
#dataset=self.dataset[Model.in_columns]
#dataset=self.dataset[['Humidity','TemperatureF','Sea Level PressureIn','PrecipitationIn','Dew PointF','Value']]
#PCA
if method=='PCA':
sklearn_pca = sklearnPCA(n_components=nr)
reduced = sklearn_pca.fit_transform(dataset)
#Factor Analysis
elif method=='FactorAnalysis':
fa=FactorAnalysis(n_components=nr)
reduced=fa.fit_transform(dataset)
#kernel pca with rbf kernel
elif method=='KPCArbf':
kpca=KernelPCA(nr,kernel='rbf')
reduced=kpca.fit_transform(dataset)
#kernel pca with poly kernel
elif method=='KPCApoly':
kpca=KernelPCA(nr,kernel='poly')
reduced=kpca.fit_transform(dataset)
#kernel pca with cosine kernel
elif method=='KPCAcosine':
kpca=KernelPCA(nr,kernel='cosine')
reduced=kpca.fit_transform(dataset)
#kernel pca with sigmoid kernel
elif method=='KPCAsigmoid':
kpca=KernelPCA(nr,kernel='sigmoid')
reduced=kpca.fit_transform(dataset)
#ICA
elif method=='IPCA':
ipca=IncrementalPCA(nr)
reduced=ipca.fit_transform(dataset)
#Fast ICA
elif method=='FastICAParallel':
fip=FastICA(nr,algorithm='parallel')
reduced=fip.fit_transform(dataset)
elif method=='FastICADeflation':
fid=FastICA(nr,algorithm='deflation')
reduced=fid.fit_transform(dataset)
elif method == 'All':
self.dimensionalityReduction(nr=nr)
return self
self.ModelInputs.update({method:reduced})
self.datasetsAvailable.append(method)
return self
def plotPCA(labels, data, inputFile, outputFile, store=False):
# apply PCA
sklearn_pca = sklearnPCA(n_components=2)
newData = sklearn_pca.fit_transform(data)
# get x and y values
xval = newData[:, 0]
yval = newData[:, 1]
lbls = set(labels)
fig1 = plt.figure(1)
# plot for each label
for lbl in lbls:
cond = [i for i, x in enumerate(labels) if x == lbl]
plt.plot(xval[cond],
yval[cond],
linestyle='none',
marker='o',
label=lbl)
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(numpoints=1, loc=0)
plt.subplots_adjust(bottom=.20, left=.20)
fig1.suptitle("PCA plot for centroids in " + inputFile.split("/")[-1],
fontsize=20)
# if PCA output parameter given then store the plot else display it
if store:
fig1.savefig("_".join(
[outputFile, inputFile.split("/")[-1].split(".")[0]]) + ".png")
else:
plt.show()
def vPca(filepath):
df = pd.read_csv(filepath_or_buffer=filepath, sep=',')
x = df.ix[:, :].values
x = x.transpose()
sklearn_pca = sklearnPCA(n_components=2)
x2 = sklearn_pca.fit_transform(x)
plots(x2)
def PCA(X, labels):
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X)
fig = plt.figure()
# fig.suptitle(band, fontweight = 'bold')
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
km = KMeans(n_clusters=2)
km.fit(Y_sklearn)
#ax.scatter(Y_sklearn[:,0], Y_sklearn[:,1])
#tresh = [0 if i >4 else 1 for i in Y_sklearn[:,0]]
colors = ['blue', 'red']
for idx, row in enumerate(Y_sklearn):
ax2.scatter(row[0], row[1], color=colors[km.labels_[idx]], alpha=0.5)
ax1.plot(X[idx, :], color=colors[km.labels_[idx]], alpha=0.2)
labels.iloc[km.labels_ == 1].to_csv(
'/Users/ryszardcetnarski/Desktop/Nencki/Badanie_NFB/Dane/miesniowcy_pca.csv '
)
return km.labels_, labels.iloc[km.labels_ == 1]
def PCA(x,x_test):
X_std = StandardScaler().fit_transform(x)
sklearn_pca = sklearnPCA(n_components='mle', svd_solver='full')
x = sklearn_pca.fit_transform(X_std)
x_test = sklearn_pca.transform(x_test)
return x,x_test
def ABC_summaryStatistics_PCA(Surveys):
""" Heavily inspired by https://plot.ly/ipython-notebooks/principal-component-analysis/ """
[A, B] = Surveys
newdist = lambda x: dbc.measurand( x.Dproj_pix()/x.GCl.R200(), 'Dproj', label='$D_\mathrm{proj,rel}$', un='$R_{200}$' )
plotmeasures = [lambda x: x.LLS, lambda x: x.P_rest, lambda x: x.Mach, newdist]
X1 = A.fetch_pandas(plotmeasures, surname=False).dropna().as_matrix() #.data() #, kwargs_FilterCluster={}, kwargs_FilterObjects={}
X2 = B.fetch_pandas(plotmeasures, surname=False).dropna().as_matrix() #.data() #, kwargs_FilterCluster={}, kwargs_FilterObjects={}
X1_std = StandardScaler().fit_transform(X1)
X2_std = StandardScaler().fit_transform(X2)
# # http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html
# plt.cla()
# pca = decomposition.PCA(n_components=3)
# pca.fit(X)
# X = pca.transform(X)
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X1_std)
""" This gives you a proxy for the average summed square error in the 2-D dimensional reduction via pca """
distance = np.sum(Y_sklearn**2)/len(Y_sklearn[0])
return distance
def Q1(self):
# part one
class1 = np.random.multivariate_normal(self.m1, self.cov, 1000).T
class2 = np.random.multivariate_normal(self.m2, self.cov, 1000).T
plt.plot(class1[0,:], class1[1,:], 'x')
plt.plot(class2[0,:], class2[1,:], 'x')
# part two : calculate pca
samples = np.concatenate((class1, class2), axis=1)
mlab_pca = mlabPCA(samples.T)
plt.figure(2)
plt.plot(mlab_pca.Y[0:1000, 0], 'o', markersize=7, color='blue', alpha=0.5, label='class1')
plt.plot(mlab_pca.Y[1000:2000, 0], '^', markersize=7, color='yellow', alpha=0.5, label='class2')
# part three
plt.figure(1)
sklearn_pca = sklearnPCA(n_components=1)
sklearn_transf = sklearn_pca.fit_transform(samples.T)
p = sklearn_pca.inverse_transform(sklearn_transf)
plt.figure(1)
plt.plot(p[0:1000, 0], p[0:1000, 1], 'x')
plt.plot(p[1000:2000, 0], p[1000:2000, 1], 'x')
error = ((p - samples.T) ** 2).mean()
print((error))
print (np.math.sqrt (error))
plt.show()
def pcaTransform(context, mesh, features, K=5):
# X_std = features;#StandardScaler().fit_transform(X);
X_std = StandardScaler().fit_transform(features)
sklearn_pca = sklearnPCA(n_components=K)
Y_sklearn = sklearn_pca.fit_transform(X_std)
mu = sklearn_pca.mean_
mu.shape = (mu.shape[0], 1)
D = sklearn_pca.explained_variance_
D_ratio = sklearn_pca.explained_variance_ratio_
V = sklearn_pca.components_
print('*' * 40)
print('DATA ENTRIES SHAPE ::: ', features.shape)
print('MEAN MATRIX SHAPE ::: ', mu.shape)
print('EIGEN VALUES SHAPE ::: ', D.shape)
print('EIGEN VECTORS SHAPE ::: ', V.shape)
print('TRANSFORMED SHAPE ::: ', Y_sklearn.shape)
sio.savemat(
bpy.path.abspath('%s/%s.mat' % (mesh.signatures_dir, mesh.name)), {
'eigenvectors': V.T,
'eigenvalues': D,
'mu': mu,
'X': X_std,
'XMinusMu': (X_std.T - mu),
'transformed': Y_sklearn
})
print('FINISHED SAVING ::: %s/%s.mat' % (mesh.signatures_dir, mesh.name))
return mu, Y_sklearn
def f(train,threshold,test):
hi=h(train)
h_score=pd.DataFrame(hi, index=np.array(range(1,21149)))
gene_ls=h_score.index[h_score.iloc[:,0]>1].tolist()
candidate_genes=['V{0}'.format(element) for element in gene_ls]
# qualified genes were selected
stdsc = preprocessing.StandardScaler()
np_scaled_train = stdsc.fit_transform(train.loc[:,candidate_genes])
np_scaled_test = stdsc.transform(test.loc[:,candidate_genes])
pca = sklearnPCA(n_components=1)
X_train_pca = pca.fit_transform(np_scaled_train) # This is the result
X_test_pca = pca.transform(np_scaled_test)
eigen_val=pca.explained_variance_ #eigen value is the explained variance
#assign pca score to the test dataset
test=test.assign(w=pd.Series(np.ones(len(test.patient_id))))
test['w']=X_test_pca
testset_surv=test[['event_free_survival_time_days','death','w']]
#do cox-regression
# Using Cox Proportional Hazards model
cph = CoxPHFitter()
cph.fit(testset_surv,'event_free_survival_time_days',event_col='death')
return cph.print_summary()
def format_data(self):
kdd_train_data = np.concatenate([
self.train_kdd_numeric, self.train_kdd_binary,
self.train_kdd_nominal
],
axis=1)
kdd_test_data = np.concatenate([
self.test_kdd_numeric, self.test_kdd_binary, self.test_kdd_nominal
],
axis=1)
kdd_train_data = np.concatenate(
[kdd_train_data, self.train_kdd_label_2classes], axis=1)
# kdd_test_data = np.concatenate([self.test_kdd_numeric, self.test_kdd_binary, self.test_kdd_nominal, self.test_kdd_label_2classes], axis=1)
kdd_test_data = np.concatenate(
[kdd_test_data, self.test_kdd_label_2classes], axis=1)
self.X_train, self.X_test, y_train, y_test = kdd_train_data[:, :
-1], kdd_test_data[:, :
-1], kdd_train_data[:,
-1], kdd_test_data[:,
-1]
data_pca = sklearnPCA(n_components=15)
data_pca = data_pca.fit(self.X_train)
# numeric_pca = numeric_pca.fit(np.concatenate((self.train_kdd_numeric, self.test_kdd_numeric), axis=0))
self.X_train = data_pca.transform(self.X_train)
self.X_test = data_pca.transform(self.X_test)
self.y_train = np.array(list(map(int, y_train)))
self.y_test = np.array(list(map(np.int64, y_test)))
def run_pca(expression):
# Load Expression data
df = pd.read_table(expression, header=0, index_col=0)
run_ids = list(df.columns.values)
dataMatrix = np.transpose(np.array(df))
run_ids = [s.replace('.htseq', '') for s in run_ids]
# Run PCA
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(
preprocessing.maxabs_scale(dataMatrix, axis=0))
with sns.axes_style("whitegrid"):
for run, pca_data in zip(run_ids, sklearn_transf):
plt.plot(pca_data[0],
pca_data[1],
'o',
markersize=7,
alpha=0.5,
color='gray')
plt.text(pca_data[0], pca_data[1], run)
plt.xlabel('PC 1 (%0.2f %%)' %
(sklearn_pca.explained_variance_ratio_[0] * 100))
plt.ylabel('PC 2 (%0.2f %%)' %
(sklearn_pca.explained_variance_ratio_[1] * 100))
plt.show()
def PCA(df, class_name):
# Cannot do PCA on an empty matrix
if len(list(df)) < 2:
return df, [], [], []
# Figure out which columns can be considered (only float or int columns but not the class column)
cols = []
for item in list(df):
if 'float' in df[item].dtypes.name:
if item != class_name:
cols.append(df.columns.get_loc(item))
# Get new dataframe
df_new = df[df.columns[cols]]
# Set this as the data to analyze
X = df_new.values
# Standardize the data
X_std = StandardScaler().fit_transform(X)
# Do PCA
pca = sklearnPCA(n_components=len(list(df_new)))
Y = pca.fit_transform(X_std)
## Get variance contributions
var_exp = pca.explained_variance_ratio_
cum_var_exp = pca.explained_variance_ratio_.cumsum()
return Y, var_exp, cum_var_exp
def pca(self,winSize):
data = np.zeros((len(self.data),len(self.data['FUNC'][winSize])))
i=0
for dEl in sorted(self.data):
self.data[dEl][winSize] = normalizeMaxMin(self.data[dEl][winSize])
data[i] = self.data[dEl][winSize]
i+=1
X_std = StandardScaler().fit_transform(np.transpose(data))
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
traces = []
trace = go.Scatter(
x=Y_sklearn[:,0],
y=Y_sklearn[:,1],
mode='markers',
marker = go.Marker(
size=12,
line= go.Line(
color='rgba(217, 217, 217, 0.14)',
width=0.5),
opacity=0.8))
traces.append(trace)
data = go.Data(traces)
layout = go.Layout(xaxis = go.XAxis(title='PC1', showline=False),
yaxis = go.YAxis(title='PC2', showline=False))
fig = go.Figure(data=data, layout=layout)
if self.outputType=='file':
print(py.plot(fig, filename='pca.html'))
else:
return py.plot(fig, output_type='div')
def dataframe_components(df2,lon,columns):
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import metrics
from sklearn import cross_validation
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA as sklearnPCA
X=df2.values
from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(X)
pca=sklearnPCA(n_components=lon).fit_transform(X_std)
list_comp_pca=[]
# CREACCION DATAFRAME CON COMPONENTES PRINCIPALES
for i in range(0,lon):
v="Componente"+str(i)
list_comp_pca.append(v)
dd1=pd.DataFrame(X_std,columns=columns)
dd2=pd.DataFrame(pca,columns=list_comp_pca)
df3=pd.concat([dd1,dd2],axis=1)
return df3
def pca(self):
# remove WHERE when table cleaned up to remove header rows
statement = (
"""SELECT transcript_id, TPM, sample_id FROM %s
where transcript_id != 'Transcript' """
% self.table
)
# fetch data
df = self.getDataFrame(statement)
# put dataframe so row=genes, cols = samples, cells contain TPM
pivot_df = df.pivot("transcript_id", "sample_id")["TPM"]
# filter dataframe to get rid of genes where TPM == 0 across samples
filtered_df = pivot_df[pivot_df.sum(axis=1) > 0]
# add +1 to counts and log transform data.
logdf = np.log(filtered_df + 0.1)
# Scale dataframe so variance =1 across rows
logscaled = sklearn_scale(logdf, axis=1)
# turn array back to df and add transcript id back to index
logscaled_df = pd.DataFrame(logscaled)
logscaled_df.index = list(logdf.index)
# Now do the PCA - can change n_components
sklearn_pca = sklearnPCA(n_components=self.n_components)
sklearn_pca.fit(logscaled_df)
index = logdf.columns
return sklearn_pca, index
def pca_analysis(indexname,dataframe):
df = dataframe
column_count = len(df.columns)
X = df.ix[:,1:column_count].values
zip = df.ix[:,0].values
#Standardize Data
X_std = StandardScaler().fit_transform(X)
#Generate PCA Components
sklearn_pca = sklearnPCA(n_components=1)
Y_sklearn = sklearn_pca.fit_transform(X_std)
explained_ratio = sklearn_pca.explained_variance_ratio_
covariance_array = sklearn_pca.get_covariance()
df_final = pd.DataFrame({'zip5':zip,indexname:Y_sklearn[:,0]})
#Normalize Data on a 0 to 1 scale
#zip5_final = df_final['zip5'].values
#minmax_scale = preprocessing.MinMaxScaler().fit(df_final[[indexname]])
#minmax = minmax_scale.transform(df_final[[indexname]])
#df_minmax = pd.DataFrame({'zip5':zip5_final,indexname:minmax[:,0]})
return df_final
def __init__(self):
""" c'tor """
self.pca = sklearnPCA(n_components=2)
self.fit_executed = False
self.X_tr = None
self.Xn_tr = None
def testPCA(self, dist):
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = -1 * sklearn_pca.fit_transform(dist)
print('MYPCA')
print(self.PCA(dist, 2)[0])
print('PCALibrary')
print(sklearn_transf)
def read_dataset(Normalize=1):
data = pd.read_csv('../../Dataset/Iris/Iris_2_classes.csv') #read dataset
data['Species'] = data['Species'].replace(
["Iris-setosa", "Iris-versicolor"], (0, 1)) #encode label
y = data['Species'] #lable
x = data.drop(['Species', 'Id'], axis=1) #drop lable and id
x = np.asarray(x) #put it in array
y = np.asarray(y) #put it in array
x = np.nan_to_num(x) #convert any nan to 0
train_x, test_x, train_y, test_y = train_test_split(
x, y, test_size=0.2,
random_state=50) #split dataset to 80 train and 20 test
if Normalize == 1: #normalize dataset
scaler = MinMaxScaler()
train_x = scaler.fit_transform(train_x)
test_x = scaler.transform(test_x)
# draw data
pca = sklearnPCA(n_components=2) # 2-dimensional PCA
transX = pd.DataFrame(pca.fit_transform(x))
plt.scatter(transX[y == 0][0], transX[y == 0][1], label='Class 1', c='red')
plt.scatter(transX[y == 1][0],
transX[y == 1][1],
label='Class 2',
c='blue')
plt.legend()
plt.show()
##################
return x, y, train_x, train_y, test_x, test_y
def pcaWiki(self,file):
self.formDataPCA(file)
X_std = StandardScaler().fit_transform(self.X)
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
traces = []
for name in self.names:
trace = go.Scatter(
x=Y_sklearn[self.y==name,0],
y=Y_sklearn[self.y==name,1],
mode='markers',
name=name,
marker=go.Marker(
size=12,
line=go.Line(
color='rgba(217, 217, 217, 0.14)',
width=0.5),
opacity=0.8))
traces.append(trace)
data = go.Data(traces)
layout = go.Layout(xaxis=go.XAxis(title='PC1', showline=False),
yaxis=go.YAxis(title='PC2', showline=False))
fig = go.Figure(data=data, layout=layout)
if (self.outputType == 'file'):
print(py.plot(fig,filename='pca'))
else:
return (py.plot(fig,include_plotlyjs='False',output_type='div'))
def main():
with open('dataset.pkl', 'rb') as f:
xr, y, features = pickle.load(f)
#y = xr.label
#Xw = xr.drop('label',axis = 1 )
Xw = xr
Xw = Xw.fillna(method='ffill')
Xw = Xw.fillna(method='bfill')
#ax = sns.countplot(y, label="Count")
#plt.show()
Wk, Tr, Bs, Cr, Nt, Bk = y.value_counts()
print('Number of Bike: ',Bk)
print('Number of Bus: ',Bs)
print('Number of Car: ',Cr)
print('Number of Nothing: ',Nt)
print('Number of Train: ',Tr)
print('Number of Walk: ',Wk)
pca = sklearnPCA(n_components=2) #2-dimensional PCA
transformed = pd.DataFrame(pca.fit_transform(Xw))
plt.scatter(transformed[y=='Walk'][0], transformed[y=='Walk'][1], label='Walk', c='darkgreen')
plt.scatter(transformed[y=='Bike'][0], transformed[y=='Bike'][1], label='Bike', c='red')
plt.scatter(transformed[y=='Train'][0], transformed[y=='Train'][1], label='Train', c='yellow')
plt.scatter(transformed[y=='Bus'][0], transformed[y=='Bus'][1], label='Bus', c='blue')
plt.scatter(transformed[y=='Car'][0], transformed[y=='Car'][1], label='Car', c='lightgreen')
plt.scatter(transformed[y=='Nothing'][0], transformed[y=='Nothing'][1], label='Nothing', c='black')
plt.legend()
plt.show()
def pca(X, y, labels, pic_file, PCA=True):
from mpl_toolkits.mplot3d import Axes3D
# fit the data for PCA and plotting
# when PCA=False, plot the original data into 3D
pca = sklearnPCA (n_components = 3)
if PCA==True: X_pca = pd.DataFrame (pca.fit_transform (X))
else: X_pca=X
# colors and markers for each class
colors = {0:"b", 1:"r", 2:"g", 3:"c", 4:"m", 5:"k"}
markers = {0:"o", 1:"^", 2:"D", 3:"*", 4:"x", 5:"p"}
num_labels = len(labels)
#Set a figure object
ans=input("Save 3D plot of samples as a picture? (y/n): ")
if (ans=="y") or (ans=="Y"): save=True
else: save=False
fig = plt.figure (figsize = (10, 10))
ax = fig.add_subplot (111, projection = "3d")
for i in range(num_labels):
X_in_cls=X_pca[y == labels[i]]
ax.scatter(X_in_cls[0], X_in_cls[1], X_in_cls[2], \
c = colors[i], marker = markers[i], label = labels[i])
ax.legend ()
plt.title (pic_file.split("_")[0])
plt.show()
if save==True:
fig.savefig("%s%s_pca.png" % (VAR.out_path, pic_file), \
bbox_inches="tight")
def cluster_model(users_data, num_cluster=3):
array_users = users_data.values
X = array_users[:, 1:17]
X_std = StandardScaler().fit_transform(X)
sklearn_pca = sklearnPCA(n_components=3)
Y_sklearn = sklearn_pca.fit_transform(X_std)
eigenValues = sklearn_pca.explained_variance_ratio_
loadings = sklearn_pca.components_
mu = np.mean(X, axis=0)
nComp = 2
Xhat = np.dot(
sklearn_pca.transform(X)[:, :nComp],
sklearn_pca.components_[:nComp, :])
Xhat = mu + Xhat
Xhat = pd.DataFrame(Xhat)
# X = PCA on previous data
X = Xhat.ix[:, '0':'1']
k = num_cluster # Define the number of clusters in which we want to partion the data
kmeans = KMeans(n_clusters=k) # Run the algorithm kmeans
kmeans.fit(X)
##sklearn.preprocessing.StandardScaler
centroids = kmeans.cluster_centers_ # Get centroid's coordinates for each cluster
labels = kmeans.labels_ # Get labels assigned to each data
final_labels = users_data[['user_id']]
final_labels['labels'] = pd.DataFrame(labels)
return final_labels
def run_pca(table, n_components):
'''
##This function is broken!##
Runs PCA on a given table for a given number of components
Params:
table (2d array): array of traces
n_components (int): Number of components to be visualized
Returns:
covar_matrix: Covariance matrix
variance (list): list of variances
components (list): list of components
'''
#calculate variance explained and cumulative variance explained
covar_matrix = sklearnPCA(n_components=10)
covar_matrix.fit(table)
variance = covar_matrix.explained_variance_ratio_
var = np.cumsum(
np.round(covar_matrix.explained_variance_ratio_, decimals=3) * 100)
#print graph of the variance explained with [n] features
plt.ylabel('%variance explained')
plt.xlabel('# of principle components')
plt.title('Variance Explained')
## plt.ylim(70,100.5)
plt.style.context('seaborn-whitegrid')
# plt.plot(variance[:n_components])
variance = covar_matrix.explained_variance_ratio_
components = covar_matrix.components_
return covar_matrix
return variance
return components
def pca_built(self, all_samples):
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(all_samples.T)
sklearn_transf = sklearn_transf * (-1)
plt.plot(sklearn_transf[0:20, 0],
sklearn_transf[0:20, 1],
'o',
markersize=7,
color='yellow',
alpha=0.5,
label='class1')
plt.plot(sklearn_transf[20:40, 0],
sklearn_transf[20:40, 1],
'^',
markersize=7,
color='black',
alpha=0.5,
label='class2')
plt.xlabel('x_values')
plt.ylabel('y_values')
plt.xlim([-4, 4])
plt.ylim([-4, 4])
plt.legend()
plt.title('Transformed samples with class labels from built PCA')
plt.draw()
plt.show()
def implement_pca_betweem_two_frames(image1, image2):
#read image
pic1 = cv2.imread(image1)
pic2 = cv2.imread(image2)
#convert BGR to Gray
prvs = cv2.cvtColor(pic1, cv2.COLOR_BGR2GRAY)
next = cv2.cvtColor(pic2, cv2.COLOR_BGR2GRAY)
#calculate optical flow
flow = cv2.calcOpticalFlowFarneback(prvs, next, None, 0.5, 3, 15, 3, 5,
1.2, 0)
#obtain angle matrix: _ is magnitude and angle_matrix is measure by degree now.
_, angle_matrix = cv2.cartToPolar(flow[..., 0],
flow[..., 1],
angleInDegrees=True)
#implement normal PCA based on the coarse foreground
sklearn_pca = sklearnPCA()
angle_std = StandardScaler().fit_transform(angle_matrix)
sklearn_pca.fit_transform(angle_std)
#convert to uint8
pca_implement = angle_std.astype(np.uint8)
#write image
cv2.imwrite('pca_fore_ground_matrix_' + str(image1) + '.png',
pca_implement)
#destroy table
cv2.destroyAllWindows()
def pca(self):
# remove WHERE when table cleaned up to remove header rows
statement = ("""SELECT transcript_id, TPM, sample_id FROM %s
where transcript_id != 'Transcript' """ % self.table)
# fetch data
df = self.getDataFrame(statement)
# put dataframe so row=genes, cols = samples, cells contain TPM
pivot_df = df.pivot('transcript_id', 'sample_id')['TPM']
# filter dataframe to get rid of genes where TPM == 0 across samples
filtered_df = pivot_df[pivot_df.sum(axis=1) > 0]
# add +1 to counts and log transform data.
logdf = np.log(filtered_df + 0.1)
# Scale dataframe so variance =1 across rows
logscaled = sklearn_scale(logdf, axis=1)
# turn array back to df and add transcript id back to index
logscaled_df = pd.DataFrame(logscaled)
logscaled_df.index = list(logdf.index)
# Now do the PCA - can change n_components
sklearn_pca = sklearnPCA(n_components=self.n_components)
sklearn_pca.fit(logscaled_df)
index = logdf.columns
return sklearn_pca, index
def kmeans():
yeast_t = 7
yeast_k = 6
yeastData = np.empty([614, 7], dtype = float)
with open('YeastGene.csv', 'rb') as yeastdata:
yeastreader = csv.reader(yeastdata, delimiter=',')
i = 0
for row in yeastreader:
yeastData[i] = row
i += 1
#print yeastData
yeastCentroid = np.empty([yeast_k, 7], dtype = float)
with open('YeastGene_Initial_Centroids.csv', 'rb') as yeastdata:
yeastreader = csv.reader(yeastdata, delimiter=',')
i = 0
for row in yeastreader:
yeastCentroid[i] = row
i += 1
#print yeastCentroid
for t in range(0, yeast_t):
yeast_c = [[] for i in range(0,yeast_k)]
minCentroid = []
for arr in yeastData:
for cen in yeastCentroid:
minCentroid.append(np.linalg.norm(arr - cen))
yeast_c[minCentroid.index(min(minCentroid))].append(arr)
minCentroid = []
for k in range(0,yeast_k):
yeastCentroid[k] = [float(sum(l))/len(l) for l in zip(*yeast_c[k])]
#print "The new yeast Centroid values\n"
#print yeastCentroid
#print "The cluster sizes are - "
print len(yeast_c[0]), len(yeast_c[1]), len(yeast_c[2]), len(yeast_c[3]), len(yeast_c[4]), len(yeast_c[5])
clusters = np.zeros([614, 7], dtype=float)
prev_len = 0
for i in range(0,6):
for j in range(0,len(yeast_c[i])):
clusters[prev_len] = yeast_c[i][j]
prev_len += 1
sklearn_pca = sklearnPCA(n_components = 2)
transf = sklearn_pca.fit_transform(clusters)
plt.plot(transf[0:140, 0], transf[0:140, 1],'*', markersize = 7, color='blue', alpha=0.5, label='cluster 1')
plt.plot(transf[140:191, 0], transf[140:191, 1],'*', markersize = 7, color='red', alpha=0.5, label='cluster 2')
plt.plot(transf[191:355, 0], transf[191:355, 1],'*', markersize = 7, color='green', alpha=0.5, label='cluster 3')
plt.plot(transf[355:376, 0], transf[355:376, 1],'*', markersize = 7, color='indigo', alpha=0.5, label='cluster 4')
plt.plot(transf[376:538, 0], transf[376:538, 1],'*', markersize = 7, color='yellow', alpha=0.5, label='cluster 5')
plt.plot(transf[538:614, 0], transf[538:614, 1],'*', markersize = 7, color='black', alpha=0.5, label='cluster 6')
plt.xlim([-10, 10])
plt.ylim([-10, 10])
plt.legend()
plt.title("Kmeans")
plt.show()
def dimensionalityReduction(self,nr=5):
'''It applies all the dimensionality reduction techniques available in this class:
Techniques available:
'PCA'
'FactorAnalysis'
'KPCArbf','KPCApoly'
'KPCAcosine','KPCAsigmoid'
'IPCA'
'FastICADeflation'
'FastICAParallel'
'Isomap'
'LLE'
'LLEmodified'
'LLEltsa'
'''
dataset=self.ModelInputs['Dataset']
sklearn_pca = sklearnPCA(n_components=nr)
p_components = sklearn_pca.fit_transform(dataset)
fa=FactorAnalysis(n_components=nr)
factors=fa.fit_transform(dataset)
kpca=KernelPCA(nr,kernel='rbf')
rbf=kpca.fit_transform(dataset)
kpca=KernelPCA(nr,kernel='poly')
poly=kpca.fit_transform(dataset)
kpca=KernelPCA(nr,kernel='cosine')
cosine=kpca.fit_transform(dataset)
kpca=KernelPCA(nr,kernel='sigmoid')
sigmoid=kpca.fit_transform(dataset)
ipca=IncrementalPCA(nr)
i_components=ipca.fit_transform(dataset)
fip=FastICA(nr,algorithm='parallel')
fid=FastICA(nr,algorithm='deflation')
ficaD=fip.fit_transform(dataset)
ficaP=fid.fit_transform(dataset)
'''isomap=Isomap(n_components=nr).fit_transform(dataset)
try:
lle1=LocallyLinearEmbedding(n_components=nr).fit_transform(dataset)
except ValueError:
lle1=LocallyLinearEmbedding(n_components=nr,eigen_solver='dense').fit_transform(dataset)
try:
lle2=LocallyLinearEmbedding(n_components=nr,method='modified').fit_transform(dataset)
except ValueError:
lle2=LocallyLinearEmbedding(n_components=nr,method='modified',eigen_solver='dense').fit_transform(dataset)
try:
lle3=LocallyLinearEmbedding(n_components=nr,method='ltsa').fit_transform(dataset)
except ValueError:
lle3=LocallyLinearEmbedding(n_components=nr,method='ltsa',eigen_solver='dense').fit_transform(dataset)'''
values=[p_components,factors,rbf,poly,cosine,sigmoid,i_components,ficaD,ficaP]#,isomap,lle1,lle2,lle3]
keys=['PCA','FactorAnalysis','KPCArbf','KPCApoly','KPCAcosine','KPCAsigmoid','IPCA','FastICADeflation','FastICAParallel']#,'Isomap','LLE','LLEmodified','LLEltsa']
self.ModelInputs.update(dict(zip(keys, values)))
[self.datasetsAvailable.append(key) for key in keys ]
#debug
#dataset=pd.DataFrame(self.ModelInputs['Dataset'])
#dataset['Output']=self.ModelOutput
#self.debug['Dimensionalityreduction']=dataset
###
return self
def apply_pca(data):
from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(data)
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
return Y_sklearn
def pcaDecomp(data, normalize = True):
if normalize:
data = StandardScaler().fit_transform(data)
pca = sklearnPCA(n_components = 2)
decomp = pca.fit_transform(data)
# plt.scatter(data[:,0], data[:,1])
# plt.show()
histo2d(decomp, ranged = False)
def pca(self, samples):
'''
Apply pca from sklearn.
'''
sklearn_pca = sklearnPCA(n_components=2)
# Fit the model with samples
fit = sklearn_pca.fit(samples)
# Apply the dimensionality reduction on samples
pca = fit.transform(samples)
return pca
def pca_json(df, n_components=4, exp_var_min=.05):
sklearn_pca = sklearnPCA(n_components=n_components)
pca_points = sklearn_pca.fit_transform(df.T)
exp_var, num_pc = pc_to_keep(sklearn_pca.explained_variance_ratio_,
exp_var_min)
pca_points_df = trim_pc(pca_points, num_pc)
pca_points_df['sample'] = df.columns.values
pca_points_df = append_exp_var(pc_df=pca_points_df,
exp_var_list=exp_var,
num_pc=num_pc)
return pca_points_df
def sklearn_practice():
from sklearn.decomposition import PCA as sklearnPCA
import numpy as np
class1_sample, class2_sample = random_gen()
all_samples = np.concatenate((class1_sample, class2_sample), axis=1)
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(all_samples.T)
return sklearn_transf
def plotGraph(samples, n_samples, tags, dimensions):
colours = ['blue', 'red', 'green', 'yellow', 'black']
n_tags = len(tags)
if dimensions == '2D':
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(samples)
for i in range(n_tags):
plt.plot(sklearn_transf[i*n_samples:(i+1)*n_samples,0],sklearn_transf[i*n_samples:(i+1)*n_samples,1],\
'o', markersize=7, color=colours[i], alpha=0.5, label=tags[i])
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
# plt.xlim([-4,4])
# plt.ylim([-4,4])
plt.legend()
plt.title('PCA')
elif dimensions == '3D':
sklearn_pca = sklearnPCA(n_components=3)
sklearn_transf = sklearn_pca.fit_transform(samples)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
plt.rcParams['legend.fontsize'] = 10
for i in range(n_tags):
ax.plot(sklearn_transf[i*n_samples:(i+1)*n_samples,0], sklearn_transf[i*n_samples:(i+1)*n_samples,1],\
sklearn_transf[i*n_samples:(i+1)*n_samples,2], 'o', markersize=8, color=colours[i], alpha=0.5, label=tags[i])
plt.title('PCA')
ax.legend(loc='upper right')
# plt.savefig("%s.png" % (dimensions), bbox_inches='tight',dpi=200)
plt.show()
# plt.close()
return True
def plotGraph(samples, word, dimensions):
if dimensions == '2D':
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(samples)
plt.plot(sklearn_transf[:,0],sklearn_transf[:,1],\
'o', markersize=7, color='blue', alpha=0.5, label='')
# plt.plot(sklearn_transf[1::2,0], sklearn_transf[1::2,1],\
# '^', markersize=7, color='red', alpha=0.5, label='Matrix')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
# plt.xlim([-4,4])
plt.ylim([-.8,.8])
plt.legend()
plt.title('Word embeddings PCA')
print sklearn_transf
elif dimensions == '3D':
sklearn_pca = sklearnPCA(n_components=3)
sklearn_transf = sklearn_pca.fit_transform(samples)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
plt.rcParams['legend.fontsize'] = 10
ax.plot(sklearn_transf[:,0], sklearn_transf[:,1],\
sklearn_transf[:,2], 'o', markersize=8, color='blue', alpha=0.5, label='')
# ax.plot(sklearn_transf[:,0], sklearn_transf[:,1],\
# sklearn_transf[:,2], '^', markersize=8, alpha=0.5, color='red', label='Matrix')
plt.title('Word embeddings PCA')
ax.legend(loc='upper right')
print sklearn_transf
plt.savefig("%s-%s.png" % (word, dimensions), bbox_inches='tight', dpi=200)
plt.close()
return True
def Seleccion_Ratios(df):
import numpy as np
import pandas as pd
from sklearn import tree
#from sklearn import metrics
from sklearn import cross_validation
from sklearn.decomposition import PCA as sklearnPCA
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
# Eliminamos antes del cálculo de las PCAs las columnas target e id.
df.columns = [x.lower() for x in df.columns]
objetivo = [col for col in df.columns if 'target' in col]
objetivo = ''.join(objetivo)
dfBorrar = df[['id', objetivo]]
borrar = ['id', objetivo]
dfaux = df.drop(borrar, axis=1)
ListaColumnas= dfaux.columns
tamDf = len(dfaux.columns)
X_std = StandardScaler().fit_transform(dfaux.values)
pca=sklearnPCA(n_components=tamDf).fit_transform(X_std)
columnas_pca=[]
for i in range(0,pca.shape[0]):
v="VAR_PCA_"+str(i)
columnas_pca.append(v)
df1=pd.DataFrame(X_std,columns=ListaColumnas)
df2=pd.DataFrame(pca,columns=columnas_pca)
df_PCA=pd.concat([df1,df2],axis=1)
y = df[objetivo]
forest = RandomForestClassifier(n_estimators=250, random_state=0)
forest.fit(df_PCA, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)
indices = np.argsort(importances)[::-1]
# Obtenemos el ranking de los mejores 30
print("TOP 30:")
for f in range(30):
print("%d. Ratio %s (%f) " % (f + 1, df_PCA.columns[indices[f]], importances[indices[f]] ))
def plot(self):
self.train()
# this will get data frame in self.mllib.X_train
X = self.mllib.X_train.iloc[:,:-1]
Y = self.mllib.X_train.iloc[:,-1]
# get data in 3D axis
scaler = sklearnPCA(n_components=3).fit(X)
X = scaler.transform(X)
Y = Y.reshape(Y.shape[0],1)
X = np.append(X, Y, 1)
self.mllib.plot(X)
def pca_built(self, all_samples):
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=2)
sklearn_transf = sklearn_pca.fit_transform(all_samples.T)
sklearn_transf = sklearn_transf*(-1)
plt.plot(sklearn_transf[0:20, 0], sklearn_transf[0:20, 1], 'o', markersize=7, color='yellow', alpha=0.5, label='class1')
plt.plot(sklearn_transf[20:40, 0], sklearn_transf[20:40, 1], '^', markersize=7, color='black', alpha=0.5, label='class2')
plt.xlabel('x_values')
plt.ylabel('y_values')
plt.xlim([-4, 4])
plt.ylim([-4, 4])
plt.legend()
plt.title('Transformed samples with class labels from built PCA')
plt.draw()
plt.show()
def dim_reduction_PCA(X,n_dim):
""" Reduce the dimension by PCA.
:param X: matrix data (n*k), n is the number of samples. k is the dimension of each sample
:param n_dim: number of dimension we desired to reduce to.
:return reduced_X:matrix data(n*n_dim)
"""
try:
reduced_X = sklearnPCA(n_components=n_dim).fit_transform(X)
except:
print ("Dimension Error")
reduced_X = []
finally:
return reduced_X
def best_dimension(X,n_com = 0.8):
""" get the number of dimension
:param X: matrix data (n*k), n is the number of samples. k is the dimension of each sample
:param n_dim: number of dimension we desired to reduce to.
:return best_dimension:
"""
try:
pca = sklearnPCA(n_components=n_com)
pca.fit_transform(X)
except:
print ("Dimension Error")
return 0
finally:
return pca.n_components_
def deaPCA(df, allres=False, normalise=False, plot=True):
"""
Extract principal components from pandas dataframe and shift distribution
so that all values are strictly positive, as required for DEA.
Takes:
df: A dataframe of series to run the PCA on.
allres: Boolean. Set True if you would like to get the PCA object
returned instead of the transformed data. This can be
useful if you wish to use the entire results of the PCA.
The object is a fit_transformed sklearn.decomposition.PCA
object.
normalise: Boolean. Set True to normalise the series to a z-score
before transforming.
plot: Should the function display a plot of the variance explained?
"""
from sklearn.decomposition import PCA as sklearnPCA
if normalise:
df = normalise_df(df)
indat_pca = sklearnPCA()
indat_transf = pd.DataFrame(
indat_pca.fit_transform(df.values), index=df.index)
pca_colnames = ["PCA" + str(i) for i in indat_transf.columns]
indat_transf.columns = pca_colnames
indat_transf_pos = _all_positive(indat_transf)
if plot:
_, ax1 = plt.subplots()
ax1.plot(np.array(indat_pca.explained_variance_ratio_).cumsum())
ax1.bar(np.arange(0.1, len(indat_pca.explained_variance_ratio_), 1),
np.array(indat_pca.explained_variance_ratio_))
ax1.legend(['Cumulative variance explained',
'Variance explained by component'], loc='center right')
ax1.set_ylabel('Proportion of variance explained')
ax1.set_title('Variance explained by each principal component')
ax1.set_xlim(right=len(indat_pca.explained_variance_ratio_))
ax1.set_ylim(top=1)
if allres:
return indat_pca
else:
return indat_transf_pos
def MakeBlocksArray(band):
path ='/Users/ryszardcetnarski/Desktop/PcaResults/'
plt.style.use('seaborn-bright')
db = LoadDatabase()
all_normed = []
for name, subject in db.groupby(db.index):
blocks = ExtractBlocks(subject, 'training', band)
# return blocks
#blocks_normed =( zscore(blocks, axis = None).T - zscore(blocks, axis = None)[:,0][:, np.newaxis].T).T
blocks_normed = zscore(blocks, axis = None)
all_normed.append(pd.DataFrame(blocks_normed, index= subject.index))
all_normed = pd.concat(all_normed)
all_normed['condition'] = db['condition']
label_dict = {'plus':0,
'minus':1,
'control':2,
'sham':3}
color = ['r', 'b','grey','g']
X = all_normed.ix[:,0:10].values
y = all_normed.ix[:,10].values
X_std = StandardScaler().fit_transform(X)
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X)
fig = plt.figure()
fig.suptitle(band, fontweight = 'bold')
ax = fig.add_subplot(111)
for idx, row in enumerate(Y_sklearn):
ax.scatter(row[0], row[1], color = color[label_dict[all_normed.iloc[idx]['condition']]], alpha = 0.5)
ax.set_xlabel('Principal Component 1')
ax.set_ylabel('Principal Component 2')
ax.legend( labels=('plus', 'minus', 'control'))
legend = ax.get_legend()
legend.legendHandles[0].set_color('red')
legend.legendHandles[1].set_color('blue')
legend.legendHandles[2].set_color('green')
def PcaFilter(X, name, PLOT_ON, method):
"""Individual subjects have outlier blocks filtered out based on malahanobis distance of their pca 1st and 2nd component
(each subject is projected into their own variance space).
Returns mask array (of 0's and 1's lenght of X) indicing bad and good blocks"""
#The convention for mask arrays is 0 - inlier, 1 - outlier
#Pca decomposition into first two components
sklearn_pca = sklearnPCA(n_components=2)
pcs = sklearn_pca.fit_transform(X)
if(method == 'outlier'):
#This index corresponds to the original index on the array of time series
#last argument is the threshold of how many standard deviations away a point is considered an outlier
outlier_idx = MD_removeOutliers(pcs[:,0], pcs[:,1], 2)
#this will be used for a boolean array for filtering.
mask_array = np.zeros(len(pcs))
if(len(outlier_idx) >0 ):
mask_array[outlier_idx] = 1
if (method == 'cluster'):
mask_array = Cluster(pcs)
if(PLOT_ON):
colors = ['r', 'b']
fig = plt.figure()
fig.suptitle(name)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
#Plot PCA scores and mark outliers
ax2.scatter(pcs[:,0], pcs[:,1], c = mask_array, cmap = 'jet', s = 60, marker = 'o')
#Print variance ratio
ax2.annotate(sklearn_pca.explained_variance_ratio_,xy= (1,1), xycoords='axes fraction', horizontalalignment='right', verticalalignment='top')
#Plot original signals and mark PCA indentified outliers
for idx,row in enumerate(X):
ax1.plot(row, color =colors[int(mask_array[idx])], alpha = 0.2)
return mask_array
def PCA(X, labels):
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X)
fig = plt.figure()
# fig.suptitle(band, fontweight = 'bold')
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
km = KMeans(n_clusters=2)
km.fit(Y_sklearn)
#ax.scatter(Y_sklearn[:,0], Y_sklearn[:,1])
#tresh = [0 if i >4 else 1 for i in Y_sklearn[:,0]]
colors = ['blue', 'red']
for idx, row in enumerate(Y_sklearn):
ax2.scatter(row[0], row[1], color =colors[km.labels_[idx]], alpha = 0.5)
ax1.plot(X[idx,:], color = colors[km.labels_[idx]], alpha = 0.2)
labels.iloc[km.labels_ ==1].to_csv('/Users/ryszardcetnarski/Desktop/Nencki/Badanie_NFB/Dane/miesniowcy_pca.csv ')
return km.labels_, labels.iloc[km.labels_ ==1]
def combine_lda_pca(X, y):
sklearn_lda = LDA(n_components=2)
X_lda_sklearn = sklearn_lda.fit_transform(X, y)
sklearn_pca = sklearnPCA(n_components=2) #PCA
X_ldapca_sklearn = sklearn_pca.fit_transform(X_lda_sklearn)
plot_scikit_lda(X_ldapca_sklearn, title='LDA+PCA via scikit-learn', mirror=(-1))
# Male
list = []
# Fill the list
for i in maleIndex:
list.append(distances[column][i])
ci = meanConfidenceInterval(list)
row = pd.Series(["M", str(column), ci[0], ci[1], ci[2]])
ciDf = ciDf.append(row, ignore_index=True)
# Female
list = []
# Fill the list
for i in femaleIndex:
list.append(distances[column][i])
ci = meanConfidenceInterval(list)
row = pd.Series(["F", str(column), ci[0], ci[1], ci[2]])
ciDf = ciDf.append(row, ignore_index=True)
# Set the dataframe columns
ciDf.columns = ("gender", "distance", "mean", "lowerBound", "upperBound")
# Save the dataframe
ciDf.to_csv("confidence_intervals", index=False)
# PCA
if plotPCA:
pca = sklearnPCA(n_components=3).fit_transform(difference)
x = [p[0] for p in pca]
y = [p[1] for p in pca]
z = [p[2] for p in pca]
ax = plt.axes(projection="3d")
ax.scatter3D(x, y, z)
plt.show()
def cluster_snapshot():
#args = flask.request.args
# Get all the form arguments in the url with defaults
#new_att = str(getitem(args, 'device_button','G_Swap_Used'))
request_att=[]
if request.method == "POST":
#checked = 'att' in request.form
request_att = request.form.getlist('att')
#any_selected = bool(selected)
request_att=[x.encode('UTF8') for x in request_att]
#print(type(request_att))
#return str(request_att)
#print (new_att)
n_times=getData_N_Min_cluster(5)
#n_times=cursor
if n_times.count()==0:
print ("No Data")
#exit(1)
data=dict()
t=0
data=[]
#Target only swap space
#'compute-2-28' removed
machine=['compute-2-29', 'compute-9-30', 'compute-9-36', 'compute-9-35','compute-9-34','compute-2-23', 'compute-2-25', 'compute-2-24', 'compute-2-27', 'compute-2-26', 'compute-6-29', 'compute-6-28', 'compute-6-25', 'compute-6-24', 'compute-6-27', 'compute-6-26', 'compute-6-23', 'compute-9-33', 'compute-9-32', 'compute-22-17', 'compute-22-16', 'compute-22-15', 'compute-22-14', 'compute-22-13', 'compute-22-12', 'compute-22-11', 'compute-22-18', 'compute-7-39', 'compute-7-38', 'compute-21-29', 'compute-21-28', 'compute-21-27', 'compute-21-26', 'compute-21-25', 'compute-21-24', 'compute-21-23', 'compute-5-1', 'compute-14-1', 'compute-14-2', 'compute-14-3', 'compute-14-4', 'compute-14-6', 'compute-14-7', 'compute-14-8', 'compute-13-6', 'compute-13-5', 'compute-13-4', 'compute-7-40', 'compute-13-2', 'compute-13-1', 'compute-14-30', 'compute-5-8', 'compute-14-32', 'compute-14-33', 'compute-14-34', 'compute-14-35', 'compute-18-18', 'compute-14-37', 'compute-14-38', 'compute-18-17', 'compute-5-3', 'compute-18-15', 'compute-5-5', 'compute-18-13', 'compute-5-7', 'compute-5-6', 'compute-6-2', 'compute-3-41', 'compute-6-1', 'compute-6-6', 'compute-6-7', 'compute-6-4', 'compute-6-5', 'compute-6-8', 'compute-13-28', 'compute-13-29', 'compute-13-26', 'compute-13-27', 'compute-13-24', 'compute-13-25', 'compute-13-23', 'compute-2-10', 'compute-2-11', 'compute-2-12', 'compute-2-14', 'compute-2-15', 'compute-2-16', 'compute-2-17', 'compute-2-18', 'compute-14-40', 'compute-2-8', 'compute-2-9', 'compute-2-7', 'compute-20-40', 'compute-1-9', 'compute-1-8', 'compute-6-11', 'compute-8-40', 'compute-6-14', 'compute-6-15', 'compute-6-16', 'compute-6-17', 'compute-6-10', 'compute-6-12', 'compute-6-13', 'compute-6-18', 'compute-4-29', 'compute-4-28', 'compute-23-38', 'compute-22-2', 'compute-23-36', 'compute-23-37', 'compute-23-34', 'compute-23-35', 'compute-4-27', 'compute-23-33', 'compute-4-25', 'compute-11-18', 'compute-8-38', 'compute-8-39', 'compute-11-17', 'compute-11-16', 'compute-22-40', 'compute-1-11', 'compute-1-10', 'compute-1-13', 'compute-1-12', 'compute-1-15', 'compute-1-14', 'compute-1-17', 'compute-1-16', 'compute-5-15', 'compute-5-14', 'compute-12-8', 'compute-5-16', 'compute-5-11', 'compute-5-10', 'compute-5-13', 'compute-5-12', 'compute-12-2', 'compute-12-3', 'compute-12-1', 'compute-12-6', 'compute-12-7', 'compute-12-4', 'compute-12-5', 'compute-12-27', 'compute-12-26', 'compute-12-18', 'compute-19-37', 'compute-19-36', 'compute-12-10', 'compute-12-11', 'compute-12-12', 'compute-12-13', 'compute-12-14', 'compute-12-15', 'compute-12-16', 'compute-12-17', 'compute-20-37', 'compute-20-36', 'compute-20-35', 'compute-20-39', 'compute-20-38', 'compute-23-39', 'compute-23-32', 'compute-4-26', 'compute-23-30', 'compute-12-23', 'compute-12-22', 'compute-12-25', 'compute-12-24', 'compute-19-39', 'compute-19-38', 'compute-12-29', 'compute-12-28', 'compute-19-35', 'compute-9-27', 'compute-21-40', 'compute-9-28', 'compute-9-29', 'compute-11-40', 'compute-21-38', 'compute-21-39', 'compute-21-30', 'compute-21-33', 'compute-21-34', 'compute-21-35', 'compute-21-36', 'compute-21-37', 'compute-5-28', 'compute-5-29', 'compute-5-24', 'compute-5-25', 'compute-5-26', 'compute-5-27', 'compute-5-23', 'compute-13-18', 'compute-13-13', 'compute-13-12', 'compute-13-11', 'compute-13-10', 'compute-13-17', 'compute-13-16', 'compute-13-15', 'compute-13-14', 'compute-14-15', 'compute-22-39', 'compute-22-38', 'compute-22-30', 'compute-22-33', 'compute-22-35', 'compute-22-34', 'compute-22-37', 'compute-22-36', 'compute-7-17', 'compute-7-16', 'compute-7-18', 'compute-5-17', 'compute-14-18', 'compute-14-12', 'compute-14-13', 'compute-14-10', 'compute-14-11', 'compute-14-16', 'compute-14-17', 'compute-14-14', 'compute-5-18', 'compute-21-8', 'compute-21-1', 'compute-21-2', 'compute-21-3', 'compute-21-4', 'compute-21-5', 'compute-21-6', 'compute-21-7', 'compute-4-30', 'compute-18-14', 'compute-23-27', 'compute-23-26', 'compute-12-37', 'compute-12-38', 'compute-21-11', 'compute-5-39', 'compute-5-38', 'compute-5-37', 'compute-5-36', 'compute-5-35', 'compute-5-34', 'compute-5-33', 'compute-5-32', 'compute-5-30', 'compute-22-3', 'compute-18-35', 'compute-22-1', 'compute-18-37', 'compute-22-7', 'compute-22-6', 'compute-22-5', 'compute-22-4', 'compute-22-8', 'compute-18-38', 'compute-18-39', 'compute-20-15', 'compute-20-17', 'compute-20-16', 'compute-20-18', 'compute-9-25', 'compute-2-38', 'compute-2-39', 'compute-2-36', 'compute-2-37', 'compute-2-34', 'compute-2-35', 'compute-2-32', 'compute-2-33', 'compute-2-30', 'compute-6-32', 'compute-6-33', 'compute-6-30', 'compute-6-36', 'compute-6-37', 'compute-6-34', 'compute-6-35', 'compute-6-38', 'compute-6-39', 'compute-9-26', 'compute-21-12', 'compute-21-13', 'compute-23-16', 'compute-23-17', 'compute-21-16', 'compute-21-17', 'compute-21-14', 'compute-21-15', 'compute-21-18', 'compute-23-18', 'compute-8-18', 'compute-8-16', 'compute-8-17', 'compute-11-39', 'compute-11-38', 'compute-22-28', 'compute-22-29', 'compute-22-23', 'compute-22-26', 'compute-22-27', 'compute-22-24', 'compute-22-25', 'compute-13-8', 'compute-13-7', 'compute-19-13', 'compute-19-15', 'compute-19-14', 'compute-19-17', 'compute-19-16', 'compute-14-27', 'compute-14-26', 'compute-14-25', 'compute-14-24', 'compute-14-23', 'compute-14-36', 'compute-14-29', 'compute-14-28', 'compute-18-16', 'compute-14-39', 'compute-3-39', 'compute-3-38', 'compute-5-2', 'compute-13-39', 'compute-13-38', 'compute-13-35', 'compute-13-34', 'compute-13-37', 'compute-13-36', 'compute-13-30', 'compute-13-33', 'compute-13-32', 'compute-3-40', 'compute-6-3', 'compute-13-40', 'compute-18-36', 'compute-23-29', 'compute-23-28', 'compute-23-25', 'compute-4-32', 'compute-4-33', 'compute-4-34', 'compute-4-35', 'compute-19-40', 'compute-18-40']
for t1 in n_times:
devices=t1['data'].keys()
for d in machine:
lst=[]
lst.append(d)
for x in xrange(0,39):
lst.append(t1['data'][d][x][1])
data.append(lst)
t=t+1
res=['Device','G_Swap_Total', 'G_Swap_Free', 'G_Swap_Used', 'G_Proc_Run', 'G_Cpu_User', 'G_Cpu_Wio', 'G_Load_One', 'G_Load', 'G_Five', 'G_Load_Fifteen', 'G_Mem_Cached', 'G_Mem_Total', 'T_State', 'T_Slots', 'T_SlotsUsed', 'T_AvailMem(MB)', 'T_TotalMem(MB)/Swap', 'T_Time_Last_Rec', 'T_LoadAve', 'T_NetLoad(MB)',
'N_Status', 'N_Swap_Service', 'N_Swap_State', 'N_Swap_Info', 'N_IPMI_Service', 'N_IPMI_State', 'N_IPMI_Info', 'N_FreeSpace_Service', 'N_FreeSpace_State', 'N_FreeSpace_Info', 'N_CVMFS-OSG_Service', 'N_CVMFS-OSG_State', 'N_CVMFS-OSG_Info', 'N_CVMFS-CERN_Service', 'N_CVMFS-CERN_State', 'N_CVMFS-CERN_Info',
'N_CVMFS-CONDB_Service', 'N_CVMFS-CONDB_State', 'N_CVMFS-CONDB_Info']
att=['G_Swap_Used','G_Cpu_User', 'G_Cpu_Wio', 'G_Load_One', 'G_Load', 'G_Five', 'G_Load_Fifteen', 'G_Mem_Cached', 'T_AvailMem(MB)', 'T_LoadAve', 'T_NetLoad(MB)']
new_att=['Device','G_Swap_Used', 'G_Proc_Run', 'G_Cpu_User', 'G_Cpu_Wio', 'G_Load_One', 'G_Load', 'G_Five', 'G_Load_Fifteen', 'G_Mem_Cached', 'T_State', 'T_Slots', 'T_SlotsUsed','T_AvailMem(MB)', 'T_Time_Last_Rec', 'T_LoadAve','N_Status', 'N_Swap_State','N_IPMI_State','N_IPMI_Info','N_FreeSpace_State', 'N_CVMFS-OSG_State', 'N_CVMFS-CERN_State', 'N_CVMFS-CONDB_State']
#new_att=['Device','G_Swap_Used','T_AvailMem(MB)','G_Five','G_Cpu_Wio']
if request_att !=[]:
new_att=request_att
print (request_att)
new_index=[]
full_index=[]
for i in new_att:
full_index.append(res.index(i))
for a in att:
new_index.append(res.index(a))
new_data=[]
for d in data:
core_count=int(d[14])
if core_count!=0:
for i in new_index:
d[i]=round(float(d[i])/core_count,2)
d[i]=unicode(d[i])
tmp=[]
for i in full_index:
if i==res.index('N_IPMI_Info'):
code_in_IPMI=re.findall(r'\d+',str(d[i]))
if code_in_IPMI==[]:
d[i]='0'
else:
d[i]=code_in_IPMI[0]
tmp.append(d[i])
# tmp=[d[i] for i in full_index]
new_data.append(tmp)
df=pd.DataFrame(new_data)
df.columns=new_att
X = df.ix[:,1:len(df.columns)].values
y = df.ix[:,0].values
from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(X)
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
x_corr=[]
y_corr=[]
label=[]
x_l=[]
y_l=[]
new_dim_data=dict()
for lab in machine:
x_fact = Y_sklearn[y==lab, 0].tolist()
y_fact = Y_sklearn[y==lab, 1].tolist()
new_dim_data[lab] =[x_fact,y_fact]
x_l.append(x_fact)
y_l.append(y_fact)
# Store new dimensions in database
post={"date":datetime.datetime.utcnow(),"data":new_dim_data}
d_var=db_new_dim.data
post_id=d_var.insert_one(post).inserted_id
for x in x_l:
for x1 in x:
x_corr.append(x1)
for y in y_l:
for y1 in y:
y_corr.append(y1)
l=len(x_l[0])
for lab in machine:
for i in [lab for x in range(0,l)]:
label.append(i)
new_arr=np.array(zip(x_corr,y_corr))
k_means=KMeans(n_clusters=4)
k_means.fit(new_arr)
centroid=k_means.cluster_centers_
labels=k_means.labels_
colors=["green","red","cyan","yellow","blue"]
color_src=[]
for i in range(len(x_corr)):
color_src.append(colors[labels[i]])
#output_file("toolbar.html")
TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,tap,previewsave,box_select,poly_select,lasso_select"
source = ColumnDataSource(
data=dict(
x=x_corr,
y=y_corr,
desc=label,
# colors=color_src,
)
)
hover = HoverTool(
tooltips="""
<div>
<div>
<span style="font-size: 17px; font-weight: bold;">@desc</span>
<span style="font-size: 15px; color: #966;">[$index]</span>
</div>
<div>
<span style="font-size: 15px;">Location</span>
<span style="font-size: 10px; color: #696;">($x, $y)</span>
</div>
</div>
"""
)
#TOOLS= [BoxZoomTool(), ResetTool(),hover,ResizeTool(),WheelZoomTool()]
TOOLS=["pan,wheel_zoom,box_zoom,reset,resize",hover]
p = figure(plot_width=600, plot_height=600, tools=TOOLS,
title="Mouse over the dots")
p.circle('x', 'y', size=30, source=source,fill_color=color_src)
p.scatter(centroid[:,0],centroid[:,1], color='black')#,s=200,linewidths=5,zorder=10)
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
# For more details see:
# http://bokeh.pydata.org/en/latest/docs/user_guide/embedding.html#components
script, div = components(p, INLINE)
html = flask.render_template(
'cluster_snapshot.html',
plot_script=script,
plot_div=div,
js_resources=js_resources,
css_resources=css_resources,
)
return encode_utf8(html)
# plt.tight_layout()
# plt.show()
# break
from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(X)
print X_std
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=2)
Y_sklearn = sklearn_pca.fit_transform(X_std)
x_corr = []
y_corr = []
label = []
# print Y_sklearn
x_l = []
y_l = []
for lab in machine:
x_l.append(Y_sklearn[y == lab, 0].tolist())
y_l.append(Y_sklearn[y == lab, 1].tolist())
for x in x_l:
#from matplotlib import pyplot as plt
import numpy as np
# feature_dict = {0: 'G_Swap_Total', 1: 'G_Swap_Free', 2: 'G_Swap_Used', 3: 'G_Proc_Run', 4: 'G_Cpu_User', 5: 'G_Cpu_Wio', 6: 'G_Load_One', 7: 'G_Load',
# 8: 'G_Five', 9: 'G_Load_Fifteen', 10: 'G_Mem_Cached', 11: 'G_Mem_Total', 12: 'T_State', 13: 'T_Slots', 14: 'T_SlotsUsed', 15: 'T_AvailMem(MB)', 16: 'T_TotalMem(MB)/Swap', 17: 'T_Time_Last_Rec', 18: 'T_LoadAve', 19: 'T_NetLoad(MB)'}
from sklearn.preprocessing import StandardScaler
X_std = StandardScaler().fit_transform(X)
CLUSTER_SIZE=4
from sklearn.decomposition import PCA as sklearnPCA
sklearn_pca = sklearnPCA(n_components=CLUSTER_SIZE)
Y_sklearn = sklearn_pca.fit_transform(X_std)
x_corr=[]
y_corr=[]
label=[]
x_l=[]
y_l=[]
for lab in machine:
x_l.append(Y_sklearn[y==lab, 0].tolist())
y_l.append(Y_sklearn[y==lab, 1].tolist())
for x in x_l:
