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Tathagat Dasgupta Tathagatd96

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trees_dump = bst.get_dump(fmap = "C:\\Users\\tatha\\.spyder-py3\\featmap.txt", with_stats = True)
for trees in trees_dump:
print(trees)
xgb.plot_importance(bst, importance_type = 'gain', xlabel = 'Gain')
X_data=data.drop(["Class","Group"],axis=1)
y_data=data["Class"]
dtrain = xgb.DMatrix(X_data,y_data)
params = {
'objective':'binary:logistic',
'max-depth':2,
'silent':1,
'eta':0.5
X_data=data.drop(["Class","Group"],axis=1)
y_data=data["Class"]
dtrain = xgb.DMatrix(X_data,y_data)
params = {
'objective':'binary:logistic',
'max-depth':2,
'silent':1,
'eta':0.5
data=pd.read_csv("pima-indians-diabetes.csv")
print(data.describe())
print(data.keys())
X_data=data.drop(["Class","Group"],axis=1)
y_data=data["Class"]
variable_params = {'max_depth':[2,4,6,10], 'n_estimators':[5, 10, 20, 25], 'learning_rate':np.linspace(1e-16, 1 , 3)}
static_params = {'objective':'multi:softmax','num_class':4, 'silent':1}
bst_grid = GridSearchCV (
estimator = XGBClassifier(**static_params),
param_grid = variable_params,
scoring = "accuracy"
)
bst_grid.fit(X_data, y_data)
print("Best Accuracy:{}".format(bst_grid.best_score_))
data=pd.read_csv("pima-indians-diabetes.csv")
print(data.describe())
print(data.keys())
X_data=data.drop(["Class","Group"],axis=1)
y_data=data["Class"]
variable_params = {'max_depth':[2,4,6], 'n_estimators':[5, 10, 20, 25], 'learning_rate':np.linspace(1e-16, 1 , 3)}
static_params = {'objective':'binary:logistic', 'silent':1}
import numpy as np
import pandas as pd
from xgboost.sklearn import XGBClassifier
from sklearn.grid_search import RandomizedSearchCV
from sklearn.cross_validation import StratifiedKFold
import random
import math
samples=[] #generator examples
with tf.Session() as sess:
sess.run(init)
for epoch in range(epochs):
num_batches=mnist.train.num_examples//batch_size
for i in range(num_batches):
batch=mnist.train.next_batch(batch_size)
batch_images=batch[0].reshape((batch_size,784))
batch_images=batch_images*2-1
lr=0.001
#Do this when multiple networks interact with each other
tvars=tf.trainable_variables() #returns all variables created(the two variable scopes) and makes trainable true
d_vars=[var for var in tvars if 'dis' in var.name]
g_vars=[var for var in tvars if 'gen' in var.name]
D_trainer=tf.train.AdamOptimizer(lr).minimize(D_loss,var_list=d_vars)
G_trainer=tf.train.AdamOptimizer(lr).minimize(G_loss,var_list=g_vars)
def loss_func(logits_in,labels_in):
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits_in,labels=labels_in))
D_real_loss=loss_func(D_logits_real,tf.ones_like(D_logits_real)*0.9) #Smoothing for generalization
D_fake_loss=loss_func(D_logits_fake,tf.zeros_like(D_logits_real))
D_loss=D_real_loss+D_fake_loss
G_loss= loss_func(D_logits_fake,tf.ones_like(D_logits_fake))