def testing_run_hardcoded_RBM_pass(self):
self.rbms.append(
rbm.RBM(NUM_DATAPOINTS,
3,
visible_unit_type='gauss',
model_name=self.name + "_layer_1",
verbose=1,
main_dir='layer_1_test'))
self.rbms.append(
rbm.RBM(3,
2,
visible_unit_type='bin',
model_name=self.name + '_layer_2',
verbose=1,
main_dir='layer_2_test'))
training_set = pd.read_csv(TRAINING_SET_PATH, sep=',', header=None)
training_set = training_set.values
self.rbms[0].fit(training_set)
test_set = pd.read_csv(TEST_SET_PATH, sep=',', header=None)
test_set = test_set.values
training_set_transform = self.rbms[0].transform(training_set)
testing_tools.write_csv(
training_set_transform,
TESTING_PATH + '/L1_training_set_transformed.csv')
self.rbms[1].fit(training_set_transform)
x = self.rbms[1].transform(training_set_transform)
testing_tools.write_csv(
x, TESTING_PATH + '/L2_training_set_transformed_transformed.csv')
def test(self , dataset):
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
#sess.run(self.testfunc , feed_dict={self.X:dataset})
#print(self.layer1_W)
#x = (sess.run(self.layer3 , feed_dict={self.X:dataset}).shape)
x = sess.run(self.layer3 , feed_dict={self.X:dataset})
testing_tools.write_csv(x , 'didthiswork.csv')
def testing_load_previous_and_transform(self):
# Note - this doesn't work and i don't know why
# load up our rbms[2]
r = rbm.RBM(3,
2,
visible_unit_type='bin',
model_name=self.name + '_layer_2',
verbose=1,
main_dir='layer_2_test')
r.load_model(
[[3, 2], [22, 3]], 1,
'/home/tyler/2018_Development/Fantasy_Stats_ML/layer_2_test/models/test1_layer_2'
)
test_set = pd.read_csv(
'FFNN_Dev_Testing/L1_training_set_transformed.csv',
sep=',',
header=None)
testing_tools.write_csv(r.transform(test_set),
TESTING_PATH + '/Loaded_L2_transform.csv')
def save_RBM_data(self):
for rbm in self.rbms:
# the name of the folder we will write in
rbm_dir = self.target_directory + rbm.model_name + '/'
# get the rbm values
values = rbm.get_model_parameters()
# store 'W'
testing_tools.write_csv(values['W'], rbm_dir + 'W.csv')
testing_tools.write_csv(values['bh_'], rbm_dir + 'bh_.csv')
testing_tools.write_csv(values['bv_'], rbm_dir + 'bv_.csv')
TEST_SET_PATH = "FormattedFantasyData/2018_data.csv"
NUM_DATAPOINTS = 22
# import our training dataset into a dataframe, then immediately recast it to numpy array
# this dataset contains fantasy stats for the past decate (excluding this current season)
# and is numerical only
# the data has 22 points of data for each player
training_set = pd.read_csv(TRAINING_SET_PATH, sep=',', header=None)
training_set = training_set.values
# get our test set
test_set = pd.read_csv(TEST_SET_PATH, sep=',', header=None)
test_set = test_set.values
# initialize RBM
r = rbm.RBM(NUM_DATAPOINTS,
3,
visible_unit_type='gauss',
model_name="fantasy_position",
verbose=1,
main_dir='fantasy_test')
# fit for training set
r.fit(training_set)
# see what this puppy thinks of the test set
testing_tools.write_csv(r.transform(test_set),
'RBMTestingResults/2018_data_transform_first.csv')
print("finished")
import rbm
import pandas as pd
import testing_tools as tt
# import our data (numerical values only)
df = pd.read_csv("nums_only.csv" , sep=',' , header=None)
df = df.values # just recasts our dataframe as a numpy array without labels or column names etc
r = rbm.RBM(27 , 2 , visible_unit_type='gauss', model_name="test_model" , verbose=1, main_dir='sametest')
r.load_model([27,2] , 1, '/home/tyler/2018_Development/Fantasy_Stats_ML/sametest/models/test_model')
print(r.get_model_parameters())
tt.write_csv(r.transform(df) , 'model_trained_data_transformed_2.csv')