def configure(self, env):
import params
env.set_params(params)
print 'Install plugins';
output = os.system("/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install mobz/elasticsearch-head")
print output
output = os.system("/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install elasticsearch/elasticsearch-repository-hdfs/2.1.0-hadoop2")
print output
output = os.system("/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install royrusso/elasticsearch-HQ")
print output
elastic()
def configure(self, env):
import params
env.set_params(params)
print 'Install plugins'
output = os.system(
"/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install mobz/elasticsearch-head"
)
print output
output = os.system(
"/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install elasticsearch/elasticsearch-repository-hdfs/2.1.0-hadoop2"
)
print output
output = os.system(
"/usr/share/elasticsearch/bin/plugin -DproxyHost=proxy.ash2.symcpe.net -DproxyPort=8080 --install royrusso/elasticsearch-HQ"
)
print output
elastic()
def calling_lex(query):
client = boto3.client('lex-runtime')
response = client.post_text(botName='SearchIntent',
botAlias='$LATEST',
userId='USER',
inputText=query)
print(json.dumps(response))
if response['dialogState'] == "ReadyForFulfillment":
keywordone = response['slots']['keywordone']
keywordtwo = response['slots']['keywordtwo']
print(keywordone)
print(keywordtwo)
response = elastic.elastic(keywordone, keywordtwo)
print("response from elastic module")
print(response)
else:
response = "Inappropiate Querry"
return response
def configure(self, env, upgrade_type=None, config_dir=None):
import params
env.set_params(params)
Logger.info('Configure Elasticsearch master node')
elastic()
from ridge import ridge from lasso import lasso from elastic import elastic import utilities # Load dataset. normalized = True dataset = utilities.import_CCPP(normalized) fit, _ = my_lslr(dataset, 15000, 0.1) utilities.stats(dataset, fit, 'My LSLR') fit, _ = my_ridge(dataset, 15000, 0.1, 0.1) utilities.stats(dataset, fit, 'My Ridge') fit = lslr(dataset) utilities.stats(dataset, fit, 'Library LSLR') fit = ridge(dataset) utilities.stats(dataset, fit, 'Library Ridge') # Load dataset. normalized = False dataset = utilities.import_CCPP(normalized) fit = elastic(dataset) utilities.stats(dataset, fit, 'Library Elastic') fit = lasso(dataset) utilities.stats(dataset, fit, 'Library Lasso')
def configure(self, env, upgrade_type=None, config_dir=None):
import params
env.set_params(params)
elastic()
def configure(self, env, upgrade_type=None, config_dir=None):
import params
env.set_params(params)
elastic()
def configure(self, env):
import params
env.set_params(params)
elastic()
def configure(self, env):
import params
env.set_params(params)
# install elastic plugins
elastic(name='master')
def plot_evaluation(my_colors, lib_colors):
# Load dataset.
dataset = utilities.import_CCPP(True)
# Store constant variables.
y_real = np.array(dataset.iloc[:, 4].values)
std_patch = mpatches.Patch(color='darkslategray')
fits = []
losses = []
losses_per_epoch = []
titles = ['LSLR Loss Per Epoch', 'Ridge Loss Per Epoch']
patches = ['Our LSLR Loss', 'Our Ridge Loss']
colors = my_colors
epochs = 15000
# Calculate my regression fits.
fit, loss = my_lslr(dataset, epochs, 0.1)
fits.append(fit)
losses_per_epoch.append(loss)
losses.append(evaluate_fit(dataset, fits[0]))
fit, loss = my_ridge(dataset, epochs, 0.1, 0.1)
fits.append(fit)
losses_per_epoch.append(loss)
losses.append(evaluate_fit(dataset, fits[1]))
# Print stats.
utilities.stats(dataset, fits[0], 'My LSLR')
utilities.stats(dataset, fits[1], 'My Ridge')
# Setup plot.
fig = plt.figure()
ax = fig.add_subplot(111)
x_axis = np.linspace(0, epochs, len(losses_per_epoch[0]), endpoint=True)
ax.plot(x_axis, losses_per_epoch[1], color=colors[1], alpha=0.75)
ax.plot(x_axis, losses_per_epoch[0], color=colors[0], alpha=0.75)
ax.legend(
[mpatches.Patch(color=colors[0]),
mpatches.Patch(color=colors[1])], [patches[0], patches[1]])
ax.set(title='Loss Per Epoch', xlabel='Epoch', ylabel='Loss')
plt.show()
# Plot fit losses.
titles = ['Our LSLR', 'Our Ridge']
x_axis = np.linspace(0,
len(dataset.iloc[:, 4].values),
len(dataset.iloc[:, 4].values),
endpoint=True)
fig, ax = plt.subplots(1, 2)
for i in range(0, len(fits)):
mean = np.mean(losses[i])
print(titles[i] + ' Mean: ' + str(mean))
loss_patch = mpatches.Patch(color=colors[i])
ax[i].semilogy(x_axis, losses[i], color=colors[i])
ax[i].set_xlim(0, len(dataset.iloc[:, 4].values))
ax[i].axhline(y=np.std(y_real)**2,
color='darkslategray',
xmin=0,
xmax=len(dataset.iloc[:, 4].values),
linestyle='-')
ax[i].axhline(y=mean,
color='w',
xmin=0,
xmax=len(dataset.iloc[:, 4].values),
linestyle='-')
ax[i].text(1.05,
mean,
'{:.4f}'.format(mean),
va='center',
ha="left",
bbox=dict(alpha=0),
transform=ax[i].get_yaxis_transform())
ax[i].legend([loss_patch, std_patch],
[patches[i], 'Standard Deviation'])
ax[i].set(title=titles[i], xlabel='Value', ylabel='Logarithmic Loss')
plt.subplots_adjust(wspace=0.3)
plt.show()
# Reset variables.
fits = []
losses = []
titles = ['LSLR', 'Ridge', 'Lasso', 'Elastic Net']
patches = ['LSLR Loss', 'Ridge Loss', 'Lasso Loss', 'Elastic Net Loss']
colors = lib_colors
# Calculate regression fits from libraries.
fits.append(lslr(dataset))
losses.append(evaluate_fit(dataset, fits[0]))
fits.append(ridge(dataset))
losses.append(evaluate_fit(dataset, fits[1]))
fits.append(lasso(dataset))
losses.append(evaluate_fit(dataset, fits[2]))
fits.append(elastic(dataset))
losses.append(evaluate_fit(dataset, fits[3]))
# Print stats.
utilities.stats(dataset, fits[0], 'Library LSLR')
utilities.stats(dataset, fits[1], 'Library Ridge')
utilities.stats(dataset, fits[2], 'Library Lasso')
utilities.stats(dataset, fits[3], 'Library Elastic Net')
# Plot fit losses.
fig, ax = plt.subplots(2, 2)
fig.suptitle('Evaluation of Regression Libraries')
for i in range(0, len(fits)):
mean = np.mean(losses[i])
print(titles[i] + ' Mean: ' + str(mean))
loss_patch = mpatches.Patch(color=colors[i])
ax[int(i / 2) % 2, i % 2].semilogy(x_axis, losses[i], color=colors[i])
ax[int(i / 2) % 2, i % 2].set_xlim(0, len(dataset.iloc[:, 4].values))
ax[int(i / 2) % 2, i % 2].axhline(y=np.std(y_real)**2,
color='darkslategray',
xmin=0,
xmax=len(dataset.iloc[:, 4].values),
linestyle='-')
ax[int(i / 2) % 2, i % 2].axhline(y=mean,
color='w',
xmin=0,
xmax=len(dataset.iloc[:, 4].values),
linestyle='-')
ax[int(i / 2) % 2,
i % 2].text(1.05,
mean,
'{:.4f}'.format(mean),
va='center',
ha="left",
bbox=dict(alpha=0),
transform=ax[int(i / 2) % 2,
i % 2].get_yaxis_transform())
ax[int(i / 2) % 2, i % 2].legend([loss_patch, std_patch],
[patches[i], 'Standard Deviation'])
ax[int(i / 2) % 2, i % 2].set(title=titles[i],
xlabel='Value',
ylabel='Loss (Logarithmic)')
plt.subplots_adjust(wspace=0.3, hspace=0.7)
plt.show()
