def main():
#creates a list of all pairwise combinations of the 10 assays
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
combin_list = []
for i in range(1, 11):
combin_list_temp = combinations(a, i)
for j in combin_list_temp:
combin_list.append(j)
# the toggle no uses the supercomputers job array ID # to determine which assay combinaiton and architecture to train.
toggle_no = int(sys.argv[1])
#determine the model architecture
b_models = ['ridge', 'forest', 'svm', 'fnn']
if toggle_no < 10000:
arch = b_models[0]
elif toggle_no < 20000:
arch = b_models[1]
toggle_no = toggle_no - 10000
elif toggle_no < 30000:
arch = b_models[2]
toggle_no = toggle_no - 20000
elif toggle_no < 40000:
arch = b_models[3]
toggle_no = toggle_no - 30000
b = modelbank.assay_to_yield_model(combin_list[toggle_no], arch, 1)
b.cross_validate_model()
b.test_model()
b.plot()
b.save_predictions()
def assay_to_yield_best_arch(self):
self.compare_test = False
self.get_control = self.get_assay_control
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
combin_list = []
for i in range(1, 11):
combin_list_temp = combinations(a, i)
for j in combin_list_temp:
combin_list.append(j)
b_models = ['ridge', 'forest', 'svm', 'fnn']
# b_models=b_models[0:3]
combin_list = combin_list[0:10]
best_model_per_combin = []
for combin in combin_list:
model_list = []
for arch in b_models:
model_list.append(
modelbank.assay_to_yield_model(combin, arch, 1))
best_model_per_combin.append(self.get_best_model(model_list))
best_model = self.get_best_model(best_model_per_combin,
save=True,
plot='assay_to_yield_best_arch')
print(best_model.model_name)
def main():
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
combin_list = []
for i in range(1, 11):
combin_list_temp = combinations(a, i)
for j in combin_list_temp:
combin_list.append(j)
toggle_no = int(sys.argv[1])
# if toggle_no<12:
### seq_to_yield model using measured yields
# a_models=['ridge','forest','svm','fnn','emb_fnn_maxpool','emb_fnn_flat','emb_rnn','emb_cnn','small_emb_rnn','small_emb_atn_rnn','small_emb_cnn','small_emb_atn_cnn']
# a=modelbank.seq_to_yield_model(a_models[toggle_no],1)
# a=modelbank.final_seq_to_yield_model(a_models[toggle_no],sample_size)
# a.cross_validate_model()
# a.test_model()
# a.plot()
# ### assay_to_yield_model
b_models = ['ridge', 'forest', 'svm', 'fnn']
if toggle_no < 10000:
arch = b_models[0]
elif toggle_no < 20000:
arch = b_models[1]
toggle_no = toggle_no - 10000
elif toggle_no < 30000:
arch = b_models[2]
toggle_no = toggle_no - 20000
elif toggle_no < 40000:
arch = b_models[3]
toggle_no = toggle_no - 30000
b = modelbank.assay_to_yield_model(combin_list[toggle_no], arch, 1)
b.cross_validate_model()
def assay_to_yield_best_arch(self):
self.compare_test=False
self.get_control=self.get_assay_control
## The class variables compare_test is set to false while the get_control variable is linked to the get_assay_control function
a=[1,2,3,4,5,6,7,8,9,10]
combin_list=[]
## a function is created to map wach assay
for i in range(1,11):
combin_list_temp=combinations(a,i)
for j in combin_list_temp:
combin_list.append(j)
## Each possible combination of the 10 assays are created, combinations ranging from only 1 assay to combination with all 10.
b_models=['ridge','forest','svm','fnn']
# b_models=b_models[0:3]
combin_list=combin_list[0:10]
best_model_per_combin=[]
for combin in combin_list:
model_list=[]
for arch in b_models:
model_list.append(modelbank.assay_to_yield_model(combin,arch,1))
best_model_per_combin.append(self.get_best_model(model_list))
## For each combination of assays, the combination with the particular regression model is stored in the best_model_per_combin list
best_model=self.get_best_model(best_model_per_combin,save=True,plot='assay_to_yield_best_arch')
## Then the best model is selected form the ones compiled in the best_model_per_combin list using the get_best_model() function
## then the model is displayed as a print statement.
print(best_model.model_name)
def main():
'''
compare test performances when reducing training sample size. This version is for first paper, predicting yield from assays and one-hot encoded sequence.
'''
a = int(sys.argv[1])
if a < 4:
b = 0
elif a < 8:
a = a - 4
b = 1
elif a < 12:
a = a - 8
b = 2
elif a == 12:
b = 3
a = a - 12
else:
print('incorrect toggle number')
arch_list = ['ridge', 'svm', 'forest', 'fnn']
# size_list=[0.055,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
size_list = [0.7, 0.8, 0.9, 1]
for size in size_list:
if b == 0:
mdl = modelbank.seqandassay_to_yield_model([1, 8, 10],
arch_list[a], size)
elif b == 1: #1,5,9,12
mdl = modelbank.assay_to_yield_model([1, 8, 10], arch_list[a],
size)
elif b == 2:
mdl = modelbank.seq_to_yield_model(arch_list[a], size)
elif b == 3:
mdl = modelbank.control_to_yield_model(arch_list[a], size)
for seed in range(9): #no seed is seed=42
mdl.change_sample_seed(seed)
mdl.cross_validate_model()
mdl.limit_test_set([1, 8, 10])
mdl.test_model()
def main():
## a is a list of integers from 1-10 to represent each assay score.
a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
combin_list = []
for i in range(1, 11):
combin_list_temp = combinations(a, i)
for j in combin_list_temp:
combin_list.append(j)
## Then each possible combination of the 10 different assays. is created and stored in an array format and then added to the empty combin_list.
# the toggle no uses the supercomputers job array ID # to determine which assay combinaiton and architecture to train.
toggle_no = int(sys.argv[1])
## sys.argv[1] is the command line inputs and. this takes the input after the file name and assigns it to the toggle_no
## Depending on the magnitube of the toggle_no entered the regression model is decided.
#determine the model architecture
b_models = ['ridge', 'forest', 'svm', 'fnn']
if toggle_no < 10000:
arch = b_models[0]
## If the toggle_no is less than 10000 then a ridge regression is run
elif toggle_no < 20000:
arch = b_models[1]
toggle_no = toggle_no - 10000
## If the toggle_no is less than 20000 then a random forest regression is run and the toggle_no is decreased by 10000
elif toggle_no < 30000:
arch = b_models[2]
toggle_no = toggle_no - 20000
## If the toggle_no is less than 30000 then a support vector regression is run and the toggle_no is decreased by 20000
elif toggle_no < 40000:
arch = b_models[3]
toggle_no = toggle_no - 30000
## If the toggle_no is less than 40000 then a feedforward neural network is run and the toggle_no is decreased by 30000
b = modelbank.assay_to_yield_model(combin_list[toggle_no], arch, 1)
## Then the assay_to_yield_model object in the submodels_module.py script is accessed and the object is
## instantiated with the model architecture determined in the above if-elif-else statements and the list in toggle_no index of combin_list
## along with a sample fraction of 1.
b.cross_validate_model()
b.test_model()
## The cross_validate_models and test_model functions of the parent model class is run, these functions determine the hyperparamters
## and then using the hyperparamters train the dataset and make a test prediction
b.plot()
b.save_predictions()
mdl.cross_validate_model()
mdl.limit_test_set([1, 8, 10])
mdl.test_model()
# if __name__ == '__main__':
# main()
loss_per_model, std_per_model = [], []
arch_list = ['ridge', 'svm', 'forest', 'fnn']
for i in range(4):
cv_loss, test_loss, test_std = np.inf, np.inf, 0
for arch in arch_list:
if i == 0:
mdl = modelbank.assay_to_yield_model([1, 8, 10], arch, 1)
elif i == 1:
mdl = modelbank.weighted_assay_to_yield_model([1, 8, 10], arch, 1)
elif i == 2:
mdl = modelbank.seqandassay_to_yield_model([1, 8, 10], arch, 1)
else:
mdl = modelbank.seqandweightedassay_to_yield_model([1, 8, 10],
arch, 1)
if mdl.model_stats['cv_avg_loss'] < cv_loss:
cv_loss = mdl.model_stats['cv_avg_loss']
test_loss = mdl.model_stats['test_avg_loss']
test_std = mdl.model_stats['test_std_loss']
loss_per_model.append(test_loss)
std_per_model.append(test_std)
seq_model = modelbank.seq_to_yield_model('forest', 1)
min_test_loss = np.mean(cur_test_loss)
min_test_std = np.std(cur_test_loss)
c_mdl_test_loss.append(min_test_loss)
c_mdl_test_std.append(min_test_std)
oh_test_loss = []
oh_model = modelbank.seq_to_yield_model('forest', 1)
oh_test_loss.append(oh_model.model_stats['test_avg_loss'])
for i in range(9):
oh_model.change_sample_seed(i)
oh_test_loss.append(oh_model.model_stats['test_avg_loss'])
oh_test_std = np.std(oh_test_loss)
oh_test_loss = np.mean(oh_test_loss)
assay_test_loss = []
assay_model = modelbank.assay_to_yield_model([1, 8, 10], 'forest', 1)
assay_test_loss.append(assay_model.model_stats['test_avg_loss'])
for i in range(9):
assay_model.change_sample_seed(i)
assay_test_loss.append(assay_model.model_stats['test_avg_loss'])
assay_test_std = np.std(assay_test_loss)
assay_test_loss = np.mean(assay_test_loss)
control_model = modelbank.control_to_yield_model('ridge', 1)
control_loss = control_model.model_stats['test_avg_loss']
control_model.limit_test_set([1, 8, 10])
exploded_df, _, _ = load_format_data.explode_yield(control_model.testing_df)
exp_var = np.average(np.square(np.array(exploded_df['y_std'])))
fig, ax = plt.subplots(1, 1, figsize=[2.5, 2.5], dpi=300)
x = [-1, len(c_models)]
# Each assay combination + architecture was cross-validated to train hyper parameters, then tested on a left-out test set
# Below shows the example of training the models shown in Figure 3d of Golinski et. al 2020.
###
import submodels_module as modelbank
#define model parameters
#assays are numbered in order as found in SI table #
#model architectures for predicting yield are: 'ridge','forest','svm','fnn'
assay_mdl_param = {
'assays': [1, 8, 10],
'model_architecture': 'forest',
'sample_fraction': 1
}
#initialize model based upon model parameters
mdl = modelbank.assay_to_yield_model(**assay_mdl_param)
###
# Other model options
#
# one-hot sequence to yield model
# seq_to_yield_param={'model_architecture':'forest', 'sample_fraction':1}
# mdl=modelbank.seq_to_yield_model(**seq_to_yield_param)
# assays and sequence model
# uses same params as assay model
# mdl=modelbank.seqandassay_to_yield_model(**assay_mdl_param)
# strain only control model
# strain_only_param={'model_architecture':'ridge', 'sample_fraction':1}}
def main():
'''
compare test performances when reducing training sample size. This version is for first paper, predicting yield from assays and one-hot encoded sequence.
'''
## A command line input is required when running this program. The integer input
## should be between 0-12.
a=int(sys.argv[1])
if a<4:
b=0
## if the input is less than 4 then b value is set to 0
elif a<8:
a=a-4
b=1
## if a is between 4-8 then the b value is set to 1 and a is reduced by 4
elif a<12:
a=a-8
b=2
## if a is between 8-12 then the b value is set to 2 and a is reduced by 8
elif a==12:
b=3
a=a-12
## if a is equal to 12 then the b value is set to 3 and a is set to 0.
else:
print('incorrect toggle number')
## If the inout is out of bounds then an error message is printed.
arch_list=['ridge','svm','forest','fnn']
## A string list is created containing the names of the different regression models and stored as arch_list
# size_list=[0.055,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1]
size_list=[0.7,0.8,0.9,1]
## A float list is created containing varying amounts of sample fractions and stored as size_list
for size in size_list:
## each element in the size_list array, we check the value of the b value created in the above if-else
## statements and this dictates the kind of submodel_module.py object created
## if b = 0, then a seqandassay_to_yield_model object is created with an assay list of [1,8,10]
## a regression model dictated by the 'a' index of the arch_list and the size determined by the iteration of size_list
if b==0:
mdl=modelbank.seqandassay_to_yield_model([1,8,10],arch_list[a],size)
## if b = 1, then a assay_to_yield_model object is created with an assay list of [1,8,10]
## a regression model dictated by the 'a' index of the arch_list and the size determined by the iteration of size_list
elif b==1: #1,5,9,12
mdl=modelbank.assay_to_yield_model([1,8,10],arch_list[a],size)
## if b = 2, then a seq_to_yield_model object is created with a regression model dictated by
## the 'a' index of the arch_list and the size determined by the iteration of size_list
elif b==2:
mdl=modelbank.seq_to_yield_model(arch_list[a],size)
## if b = 3, then a control_to_yield_model object is created with a regression model dictated by
## the 'a' index of the arch_list and the size determined by the iteration of size_list
elif b==3:
mdl=modelbank.control_to_yield_model(arch_list[a],size)
for seed in range(9): #no seed is seed=42
## For each element in the int range [0,9). The sample_seed class int to the element
## Then the trial data, model data and plots are updated to reflect the new sample_seed size
mdl.change_sample_seed(seed)
## Then the best hyperparameters for the given model and seed size is determined using the cross_validate_model()
## function from the model object
mdl.cross_validate_model()
## Following this limit_test_set() function defined in the x_to_yield_model parent class to update the
## testing_df class dataframe to reflect the 1,8,10 assays.
mdl.limit_test_set([1,8,10])
## Finally using the test_model() function from the model parent class is run to
## train the model using the hyperparameters defined above and the training data to predict the testing dataset.
mdl.test_model()