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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') |
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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 |
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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 |
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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} |
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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_)) |
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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} |
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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 |
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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 |
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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) |
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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)) |
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