Commit 2787af7c authored by Bharath Ramsundar's avatar Bharath Ramsundar
Browse files

Cleanedup examples

parent d937f57b

examples/muv_keras_models.py

+0 −9
Original line number Diff line number Diff line
@@ -93,15 +93,6 @@ train_samples, valid_samples, test_samples = \ 
        featurized_samples, train_dir, valid_dir, test_dir,

        log_every_n=1000, reload=reload)



len_train_samples, len_valid_samples, len_test_samples = \

  len(train_samples), len(valid_samples), len(test_samples)

assert relative_difference(

    len(train_samples), frac_train * len(featurized_samples)) < 1e-3

assert relative_difference(

    len(valid_samples), frac_valid * len(featurized_samples)) < 1e-3

assert relative_difference(

    len(test_samples), frac_test * len(featurized_samples)) < 1e-3



# Generate datasets

print("About to create datasets")

print("MUV_tasks")

examples/muv_sklearn_models.py

+4 −23
Original line number Diff line number Diff line
@@ -110,20 +110,14 @@ full_dataset = Dataset(data_dir=full_dir, samples=featurized_samples, 
print("full_dataset.get_task_names()")

print(full_dataset.get_task_names())

y = full_dataset.get_labels()

#if os.path.exists(train_dir):

#  shutil.rmtree(train_dir)

train_dataset = Dataset(data_dir=train_dir, samples=train_samples, 

                        featurizers=featurizers, tasks=MUV_tasks,

                        verbosity=verbosity, reload=reload)

y_train  = train_dataset.get_labels()

#if os.path.exists(valid_dir):

#  shutil.rmtree(valid_dir)

valid_dataset = Dataset(data_dir=valid_dir, samples=valid_samples, 

                        featurizers=featurizers, tasks=MUV_tasks,

                        verbosity=verbosity, reload=reload)

y_valid = valid_dataset.get_labels()

#if os.path.exists(test_dir):

#  shutil.rmtree(test_dir)

test_dataset = Dataset(data_dir=test_dir, samples=test_samples, 

                       featurizers=featurizers, tasks=MUV_tasks,

                       verbosity=verbosity, reload=reload)
@@ -131,19 +125,6 @@ y_test = test_dataset.get_labels() 
len_train_dataset, len_valid_dataset, len_test_dataset = \

  len(train_dataset), len(valid_dataset), len(test_dataset)



print("len(train_samples), len(train_dataset)")

print(len(train_samples), len(train_dataset))

assert relative_difference(

    len(train_samples), len(train_dataset)) < 1e-3

print("len(valid_samples), len(valid_dataset)")

print(len(valid_samples), len(valid_dataset))

assert relative_difference(

    len(valid_samples), len(valid_dataset)) < 1e-2

print("len(test_samples), len(test_dataset)")

print(len(test_samples), len(test_dataset))

assert relative_difference(

    len(test_samples), len(test_dataset)) < 1e-2



# Transform data

print("About to transform data")

input_transformers = []
@@ -171,10 +152,10 @@ params_dict = { 


def model_builder(tasks, task_types, model_params, model_dir, verbosity=None):

  return SklearnModel(tasks, task_types, model_params, model_dir,

                      model_instance=LogisticRegression(class_weight="balanced"),

                      #model_instance=RandomForestClassifier(

                      #    class_weight="balanced",

                      #    n_estimators=500),

                      #model_instance=LogisticRegression(class_weight="balanced"),

                      model_instance=RandomForestClassifier(

                          class_weight="balanced",

                          n_estimators=500),

                      verbosity=verbosity)

model = SingletaskToMultitask(MUV_tasks, MUV_task_types, params_dict, model_dir,

                              model_builder, verbosity=verbosity)

examples/muv_tf_models.py

+0 −76
Original line number Diff line number Diff line
@@ -38,7 +38,6 @@ verbosity = "high" 


# Create some directories for analysis

# The base_dir holds the results of all analysis

#base_dir = "/scratch/users/rbharath/muv_multitask_analysis"

base_dir = "/scratch/users/rbharath/small_muv_multitask_analysis"

current_dir = os.path.dirname(os.path.realpath(__file__))

#Make directories to store the raw and featurized datasets.
@@ -130,81 +129,6 @@ for transformer in transformers: 
for transformer in transformers:

    transformer.transform(test_dataset)



## Do train/valid split.

##num_train = 2048

##num_train = 4096

##num_train = 8192

#num_train = 12800 

##num_train = 13000

##num_train = 16384 ## BROKEN

#num_valid = 1024

#X, y, w, ids = full_dataset.to_numpy()

#

#w_flat = w.flatten()

##y_flat = y.flatten()





####### DEBUG

#    num_nonzero = np.count_nonzero(y)

#    weight_nonzero = len(y)/num_nonzero

#    print("weight_nonzero")

#    print(weight_nonzero)

#    w[y_flat != 0] = weight_nonzero

####### DEBUG

#

#X, y, w, ids = X[w_flat != 0], y[w_flat != 0], w[w_flat != 0], ids[w_flat != 0]

#print("Shape after removing zeros")

#print("X.shape")

#print(X.shape)

#X_train, X_valid = X[:num_train], X[num_train:num_train+num_valid]

#y_train, y_valid = y[:num_train], y[num_train:num_train+num_valid]

#w_train, w_valid = w[:num_train], w[num_train:num_train+num_valid]

#ids_train, ids_valid = ids[:num_train], ids[num_train:num_train+num_valid]

#

#

#if os.path.exists(train_dir):

#  shutil.rmtree(train_dir)

#train_dataset = Dataset.from_numpy(train_dir, MUV_tasks, X_train, y_train,

#                                   w_train, ids_train)

#print("len(train_dataset)")

#print(len(train_dataset))

#if os.path.exists(valid_dir):

#  shutil.rmtree(valid_dir)

#valid_dataset = Dataset.from_numpy(valid_dir, MUV_tasks, X_valid, y_valid,

#                                   w_valid, ids_valid)

#print("len(valid_dataset)")

#print(len(valid_dataset))

#

#y = full_dataset.get_labels()

#train_dataset = Dataset(data_dir=train_dir, samples=train_samples, 

#                        featurizers=featurizers, tasks=MUV_tasks,

#                        verbosity=verbosity, reload=reload)

#y_train  = train_dataset.get_labels()

#

#X_train, y_train, w_train, ids_train = train_dataset.to_numpy()

#w_flat = w_train.flatten()

#X_train = X_train[w_flat != 0]

#y_train = y_train[w_flat != 0]

#ids_train = ids_train[w_flat != 0]

#w_train = w_train[w_flat != 0]

######

#w_train = np.ones_like(w_train)

######

#train_dataset = Dataset.from_numpy(train_dir2, MUV_tasks, X_train, y_train,

#                                   w_train, ids_train)

#y_train  = train_dataset.get_labels()

#

#valid_dataset = Dataset(data_dir=valid_dir, samples=valid_samples, 

#                        featurizers=featurizers, tasks=MUV_tasks,

#                        verbosity=verbosity, reload=reload)

#y_valid = valid_dataset.get_labels()

#test_dataset = Dataset(data_dir=test_dir, samples=test_samples, 

#                       featurizers=featurizers, tasks=MUV_tasks,

#                       verbosity=verbosity, reload=reload)

#y_test = test_dataset.get_labels()

#len_train_dataset, len_valid_dataset, len_test_dataset = \

#  len(train_dataset), len(valid_dataset), len(test_dataset)



transformers = []



# Fit tensorflow models

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