Tathagatd96

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')

Tathagatd96

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

Tathagatd96

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

Tathagatd96

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}

Tathagatd96

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_))

Tathagatd96

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}

Tathagatd96

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

Tathagatd96

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

Tathagatd96

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)

Tathagatd96

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))