class DataModel():
"""
Methods for loading and compressing input data
Usage:
from tybalt.data_models import DataModel
data = DataModel(filename)
"""
def __init__(self,
filename=None,
df=False,
select_columns=False,
gene_modules=None,
test_filename=None,
test_df=None):
"""
DataModel can be initialized with either a filename or a pandas
dataframe and processes gene modules and sample labels if provided.
Arguments:
filename - if provided, load gene expression data into object
df - dataframe of preloaded gene expression data
select_columns - the columns of the dataframe to use
gene_modules - a list of gene module assignments for each gene (for use
with the simulated data or when ground truth gene modules are known)
test_filename - if provided, loads testing dataset into object
test_df - dataframe of prelaoded gene expression testing set data
"""
# Load gene expression data
self.filename = filename
if filename is None:
self.df = df
else:
self.df = pd.read_table(self.filename, index_col=0)
# Load test set gene expression data if applicable
self.test_filename = test_filename
self.test_df = test_df
if test_filename is not None and test_df is None:
self.test_df = pd.read_table(self.test_filename, index_col=0)
if select_columns:
subset_df = self.df.iloc[:, select_columns]
other_columns = range(max(select_columns) + 1, self.df.shape[1])
self.other_df = self.df.iloc[:, other_columns]
self.df = subset_df
if self.test_df is not None:
self.test_df = self.test_df.iloc[:, select_columns]
if gene_modules is not None:
self.gene_modules = pd.DataFrame(gene_modules).T
self.gene_modules.index = ['modules']
self.num_samples, self.num_genes = self.df.shape
self.num_test_samples, self.num_test_genes = self.test_df.shape
assert_notice = 'train and test sets must have same number of genes'
assert self.num_genes == self.num_test_genes, assert_notice
def transform(self, how):
self.transformation = how
if how == 'zscore':
self.transform_fit = StandardScaler().fit(self.df)
elif how == 'zeroone':
self.transform_fit = MinMaxScaler().fit(self.df)
else:
raise ValueError('how must be either "zscore" or "zeroone".')
self.df = pd.DataFrame(self.transform_fit.transform(self.df),
index=self.df.index,
columns=self.df.columns)
if self.test_df is not None:
if how == 'zscore':
self.transform_test_fit = StandardScaler().fit(self.test_df)
elif how == 'zeroone':
self.transform_test_fit = MinMaxScaler().fit(self.test_df)
test_transform = self.transform_test_fit.transform(self.test_df)
self.test_df = pd.DataFrame(test_transform,
index=self.test_df.index,
columns=self.test_df.columns)
def pca(self, n_components, transform_df=False, transform_test_df=False):
self.pca_fit = decomposition.PCA(n_components=n_components)
self.pca_df = self.pca_fit.fit_transform(self.df)
colnames = ['pca_{}'.format(x) for x in range(0, n_components)]
self.pca_df = pd.DataFrame(self.pca_df,
index=self.df.index,
columns=colnames)
self.pca_weights = pd.DataFrame(self.pca_fit.components_,
columns=self.df.columns,
index=colnames)
if transform_df:
out_df = self.pca_fit.transform(transform_df)
return out_df
if transform_test_df:
self.pca_test_df = self.pca_fit.transform(self.test_df)
def ica(self, n_components, transform_df=False, transform_test_df=False):
self.ica_fit = decomposition.FastICA(n_components=n_components)
self.ica_df = self.ica_fit.fit_transform(self.df)
colnames = ['ica_{}'.format(x) for x in range(0, n_components)]
self.ica_df = pd.DataFrame(self.ica_df,
index=self.df.index,
columns=colnames)
self.ica_weights = pd.DataFrame(self.ica_fit.components_,
columns=self.df.columns,
index=colnames)
if transform_df:
out_df = self.ica_fit.transform(transform_df)
return out_df
if transform_test_df:
self.ica_test_df = self.ica_fit.transform(self.test_df)
def nmf(
self,
n_components,
transform_df=False,
transform_test_df=False,
init='nndsvdar',
tol=5e-3,
):
self.nmf_fit = decomposition.NMF(n_components=n_components,
init=init,
tol=tol)
self.nmf_df = self.nmf_fit.fit_transform(self.df)
colnames = ['nmf_{}'.format(x) for x in range(n_components)]
self.nmf_df = pd.DataFrame(self.nmf_df,
index=self.df.index,
columns=colnames)
self.nmf_weights = pd.DataFrame(self.nmf_fit.components_,
columns=self.df.columns,
index=colnames)
if transform_df:
out_df = self.nmf_fit.transform(transform_df)
return out_df
if transform_test_df:
self.nmf_test_df = self.nmf_fit.transform(self.test_df)
def nn(self,
n_components,
model='tybalt',
transform_df=False,
transform_test_df=False,
**kwargs):
# unpack kwargs
original_dim = kwargs.pop('original_dim', self.df.shape[1])
latent_dim = kwargs.pop('latent_dim', n_components)
batch_size = kwargs.pop('batch_size', 50)
epochs = kwargs.pop('epochs', 50)
learning_rate = kwargs.pop('learning_rate', 0.0005)
noise = kwargs.pop('noise', 0)
sparsity = kwargs.pop('sparsity', 0)
kappa = kwargs.pop('kappa', 1)
epsilon_std = kwargs.pop('epsilon_std', 1.0)
beta = kwargs.pop('beta', 0)
beta = K.variable(beta)
loss = kwargs.pop('loss', 'binary_crossentropy')
validation_ratio = kwargs.pop('validation_ratio', 0.1)
tied_weights = kwargs.pop('tied_weights', True)
if tied_weights and model == 'adage':
use_decoder_weights = False
else:
use_decoder_weights = True
verbose = kwargs.pop('verbose', True)
tybalt_separate_loss = kwargs.pop('separate_loss', False)
adage_comp_loss = kwargs.pop('multiply_adage_loss', False)
adage_optimizer = kwargs.pop('adage_optimizer', 'adam')
# Extra processing for conditional vae
if hasattr(self, 'other_df') and model == 'ctybalt':
y_df = kwargs.pop('y_df', self.other_df)
y_var = kwargs.pop('y_var', 'groups')
label_dim = kwargs.pop('label_dim', len(set(y_df[y_var])))
self.nn_train_y = y_df.drop(self.nn_test_df.index)
self.nn_test_y = y_df.drop(self.nn_train_df.index)
self.nn_train_y = self.nn_train_y.loc[self.nn_train_df.index, ]
self.nn_test_y = self.nn_test_y.loc[self.nn_test_df.index, ]
label_encoder = LabelEncoder().fit(self.other_df[y_var])
self.nn_train_y = label_encoder.transform(self.nn_train_y[y_var])
self.nn_test_y = label_encoder.transform(self.nn_test_y[y_var])
self.other_onehot = label_encoder.transform(self.other_df[y_var])
self.nn_train_y = to_categorical(self.nn_train_y)
self.nn_test_y = to_categorical(self.nn_test_y)
self.other_onehot = to_categorical(self.other_onehot)
self.nn_test_df = self.df.sample(frac=validation_ratio)
self.nn_train_df = self.df.drop(self.nn_test_df.index)
if model == 'tybalt':
self.tybalt_fit = Tybalt(original_dim=original_dim,
latent_dim=latent_dim,
batch_size=batch_size,
epochs=epochs,
learning_rate=learning_rate,
kappa=kappa,
epsilon_std=epsilon_std,
beta=beta,
loss=loss,
verbose=verbose)
self.tybalt_fit.initialize_model()
self.tybalt_fit.train_vae(train_df=self.nn_train_df,
test_df=self.nn_test_df,
separate_loss=tybalt_separate_loss)
features = ['vae_{}'.format(x) for x in range(0, latent_dim)]
self.tybalt_weights = (self.tybalt_fit.get_weights(
decoder=use_decoder_weights))
self.tybalt_weights = pd.DataFrame(self.tybalt_weights[1][0],
columns=self.df.columns,
index=features)
self.tybalt_df = self.tybalt_fit.compress(self.df)
self.tybalt_df.columns = features
if transform_df:
out_df = self.tybalt_fit.compress(transform_df)
return out_df
if transform_test_df:
self.tybalt_test_df = self.tybalt_fit.compress(self.test_df)
if model == 'ctybalt':
self.ctybalt_fit = cTybalt(original_dim=original_dim,
latent_dim=latent_dim,
label_dim=label_dim,
batch_size=batch_size,
epochs=epochs,
learning_rate=learning_rate,
kappa=kappa,
epsilon_std=epsilon_std,
beta=beta,
loss=loss,
verbose=verbose)
self.ctybalt_fit.initialize_model()
self.ctybalt_fit.train_cvae(train_df=self.nn_train_df,
train_labels_df=self.nn_train_y,
test_df=self.nn_test_df,
test_labels_df=self.nn_test_y)
self.ctybalt_decoder_w = (self.ctybalt_fit.get_weights(
decoder=use_decoder_weights))
features = ['cvae_{}'.format(x) for x in range(0, latent_dim)]
features_with_groups = features + [
'group_{}'.format(x)
for x in range(latent_dim, latent_dim + label_dim)
]
w = pd.DataFrame(self.ctybalt_decoder_w[1][0])
self.ctybalt_group_w = pd.DataFrame(w.iloc[:, -label_dim:])
gene_range = range(0, w.shape[1] - label_dim)
self.ctybalt_weights = pd.DataFrame(w.iloc[:, gene_range])
self.ctybalt_weights.columns = self.df.columns
self.ctybalt_weights.index = features_with_groups
self.ctybalt_df = self.ctybalt_fit.compress(
[self.df, self.other_onehot])
self.ctybalt_df.columns = features
if transform_df:
# Note: transform_df must be a list of two dfs [x_df, y_df]
out_df = self.ctybalt_fit.compress(transform_df)
return out_df
if model == 'adage':
self.adage_fit = Adage(original_dim=original_dim,
latent_dim=latent_dim,
noise=noise,
batch_size=batch_size,
epochs=epochs,
sparsity=sparsity,
learning_rate=learning_rate,
loss=loss,
verbose=verbose,
tied_weights=tied_weights,
optimizer=adage_optimizer)
self.adage_fit.initialize_model()
self.adage_fit.train_adage(train_df=self.nn_train_df,
test_df=self.nn_test_df,
adage_comparable_loss=adage_comp_loss)
features = ['dae_{}'.format(x) for x in range(0, latent_dim)]
self.adage_weights = (self.adage_fit.get_weights(
decoder=use_decoder_weights))
self.adage_weights = pd.DataFrame(self.adage_weights[1][0],
columns=self.df.columns,
index=features)
self.adage_df = self.adage_fit.compress(self.df)
self.adage_df.columns = features
if transform_df:
out_df = self.adage_fit.compress(transform_df)
return out_df
if transform_test_df:
self.adage_test_df = self.adage_fit.compress(self.test_df)
def combine_models(self,
include_labels=False,
include_raw=False,
test_set=False):
"""
Merge z matrices together across algorithms
Arguments:
test_set - if True, output z matrix predictions for test set
Output:
pandas dataframe of all model z matrices
"""
all_models = []
if hasattr(self, 'pca_df'):
if test_set:
pca_df = pd.DataFrame(self.pca_test_df,
index=self.test_df.index,
columns=self.pca_df.columns)
else:
pca_df = self.pca_df
all_models += [pca_df]
if hasattr(self, 'ica_df'):
if test_set:
ica_df = pd.DataFrame(self.ica_test_df,
index=self.test_df.index,
columns=self.ica_df.columns)
else:
ica_df = self.ica_df
all_models += [ica_df]
if hasattr(self, 'nmf_df'):
if test_set:
nmf_df = pd.DataFrame(self.nmf_test_df,
index=self.test_df.index,
columns=self.nmf_df.columns)
else:
nmf_df = self.nmf_df
all_models += [nmf_df]
if hasattr(self, 'tybalt_df'):
if test_set:
tybalt_df = self.tybalt_test_df
tybalt_df.columns = columns = self.tybalt_df.columns
else:
tybalt_df = self.tybalt_df
all_models += [tybalt_df]
if hasattr(self, 'ctybalt_df'):
if test_set:
ctybalt_df = pd.DataFrame(self.ctybalt_test_df,
index=self.test_df.index,
columns=self.ctybalt_df.columns)
else:
ctybalt_df = self.ctybalt_df
all_models += [ctybalt_df]
if hasattr(self, 'adage_df'):
if test_set:
adage_df = self.adage_test_df
adage_df.columns = columns = self.adage_df.columns
else:
adage_df = self.adage_df
all_models += [adage_df]
if include_raw:
all_models += [self.df]
if include_labels:
all_models += [self.other_df]
all_df = pd.concat(all_models, axis=1)
return all_df
def combine_weight_matrix(self):
all_weight = []
if hasattr(self, 'pca_df'):
all_weight += [self.pca_weights]
if hasattr(self, 'ica_df'):
all_weight += [self.ica_weights]
if hasattr(self, 'nmf_df'):
all_weight += [self.nmf_weights]
if hasattr(self, 'tybalt_df'):
all_weight += [self.tybalt_weights]
if hasattr(self, 'ctybalt_df'):
all_weight += [self.ctybalt_weights]
if hasattr(self, 'adage_df'):
all_weight += [self.adage_weights]
all_weight_df = pd.concat(all_weight, axis=0).T
all_weight_df = all_weight_df.rename({'Unnamed: 0': 'entrez_gene'},
axis='columns')
return all_weight_df
def compile_reconstruction(self, test_set=False):
"""
Compile reconstruction costs between input and algorithm reconstruction
Arguments:
test_set - if True, compile reconstruction for the test set data
Output:
Two dictionaries storing 1) reconstruction costs and 2) reconstructed
matrix for each algorithm
"""
# Set the dataframe for use to compute reconstruction cost
if test_set:
input_df = self.test_df
else:
input_df = self.df
all_reconstruction = {}
reconstruct_mat = {}
if hasattr(self, 'pca_df'):
# Set PCA dataframe
if test_set:
pca_df = self.pca_test_df
else:
pca_df = self.pca_df
pca_reconstruct = self.pca_fit.inverse_transform(pca_df)
pca_recon = approx_keras_binary_cross_entropy(
pca_reconstruct, input_df, self.num_genes)
all_reconstruction['pca'] = [pca_recon]
reconstruct_mat['pca'] = pd.DataFrame(pca_reconstruct,
index=input_df.index,
columns=input_df.columns)
if hasattr(self, 'ica_df'):
# Set ICA dataframe
if test_set:
ica_df = self.ica_test_df
else:
ica_df = self.ica_df
ica_reconstruct = self.ica_fit.inverse_transform(ica_df)
ica_recon = approx_keras_binary_cross_entropy(
ica_reconstruct, input_df, self.num_genes)
all_reconstruction['ica'] = [ica_recon]
reconstruct_mat['ica'] = pd.DataFrame(ica_reconstruct,
index=input_df.index,
columns=input_df.columns)
if hasattr(self, 'nmf_df'):
# Set NMF dataframe
if test_set:
nmf_df = self.nmf_test_df
else:
nmf_df = self.nmf_df
nmf_reconstruct = self.nmf_fit.inverse_transform(nmf_df)
nmf_recon = approx_keras_binary_cross_entropy(
nmf_reconstruct, input_df, self.num_genes)
all_reconstruction['nmf'] = [nmf_recon]
reconstruct_mat['nmf'] = pd.DataFrame(nmf_reconstruct,
index=input_df.index,
columns=input_df.columns)
if hasattr(self, 'tybalt_df'):
vae_reconstruct = self.tybalt_fit.decoder.predict_on_batch(
self.tybalt_fit.encoder.predict_on_batch(input_df))
vae_recon = approx_keras_binary_cross_entropy(
vae_reconstruct, input_df, self.num_genes)
all_reconstruction['vae'] = [vae_recon]
reconstruct_mat['vae'] = pd.DataFrame(vae_reconstruct,
index=input_df.index,
columns=input_df.columns)
if hasattr(self, 'adage_df'):
dae_reconstruct = self.adage_fit.decoder.predict_on_batch(
self.adage_fit.encoder.predict_on_batch(input_df))
dae_recon = approx_keras_binary_cross_entropy(
dae_reconstruct, input_df, self.num_genes)
all_reconstruction['dae'] = [dae_recon]
reconstruct_mat['dae'] = pd.DataFrame(dae_reconstruct,
index=input_df.index,
columns=input_df.columns)
return pd.DataFrame(all_reconstruction), reconstruct_mat
def compile_reconstruction_testset(self):
all_reconstruction = {}
reconstruct_mat = {}
if hasattr(self, 'pca_test_df'):
key = 'pca_test'
pca_reconstruct = self.pca_fit.inverse_transform(self.pca_test_df)
pca_recon = approx_keras_binary_cross_entropy(
pca_reconstruct, self.test_df, self.num_genes)
all_reconstruction[key] = [pca_recon]
reconstruct_mat[key] = pd.DataFrame(pca_reconstruct,
index=self.test_df.index,
columns=self.test_df.columns)
if hasattr(self, 'ica_test_df'):
key = 'ica_test'
ica_reconstruct = self.ica_fit.inverse_transform(self.ica_test_df)
ica_recon = approx_keras_binary_cross_entropy(
ica_reconstruct, self.test_df, self.num_genes)
all_reconstruction[key] = [ica_recon]
reconstruct_mat[key] = pd.DataFrame(ica_reconstruct,
index=self.test_df.index,
columns=self.test_df.columns)
if hasattr(self, 'nmf_test_df'):
key = 'nmf_test'
nmf_reconstruct = self.nmf_fit.inverse_transform(self.nmf_test_df)
nmf_recon = approx_keras_binary_cross_entropy(
nmf_reconstruct, self.test_df, self.num_genes)
all_reconstruction[key] = [nmf_recon]
reconstruct_mat[key] = pd.DataFrame(nmf_reconstruct,
index=self.test_df.index,
columns=self.test_df.columns)
if hasattr(self, 'tybalt_test_df'):
key = 'vae'
vae_reconstruct = self.tybalt_fit.decoder.predict_on_batch(
self.tybalt_fit.encoder.predict_on_batch(self.test_df))
vae_recon = approx_keras_binary_cross_entropy(
vae_reconstruct, self.test_df, self.num_genes)
all_reconstruction[key] = [vae_recon]
reconstruct_mat[key] = pd.DataFrame(vae_reconstruct,
index=self.test_df.index,
columns=self.test_df.columns)
if hasattr(self, 'adage_test_df'):
key = 'dae'
dae_reconstruct = self.adage_fit.decoder.predict_on_batch(
self.adage_fit.encoder.predict_on_batch(self.test_df))
dae_recon = approx_keras_binary_cross_entropy(
dae_reconstruct, self.test_df, self.num_genes)
all_reconstruction[key] = [dae_recon]
reconstruct_mat[key] = pd.DataFrame(dae_reconstruct,
index=self.test_df.index,
columns=self.test_df.columns)
return pd.DataFrame(all_reconstruction), reconstruct_mat
def get_modules_ranks(self, weight_df, num_components, noise_column=0):
"""
Takes a compression algorithm's weight matrix (gene by latent feature),
and reports two performance metrics:
1) mean rank sum across modules:
measures how well modules are separated into features
2) min average rank across modules:
measures how well modules of decreasing proportion are captured
"""
# Rank absolute value compressed features for each gene
weight_rank_df = weight_df.abs().rank(axis=0, ascending=False)
# Add gene module membership to ranks
module_w_df = pd.concat([weight_rank_df, self.gene_modules], axis=0)
module_w_df = module_w_df.astype(int)
# Get the total module by compressed feature mean rank
module_meanrank_df = (module_w_df.T.groupby('modules').mean()).T
# Drop the noise column and get the sum of the minimum mean rank.
# This heuristic measures, on average, how well individual compressed
# features capture ground truth gene modules. A lower number indicates
# better separation performance for the algorithm of interest
module_meanrank_minsum = (module_meanrank_df.drop(
noise_column, axis=1).min(axis=0).sum())
# Process output data
# Divide this by the total number of features in the model. Subtract by
# one to account for the dropped noise column, if applicable
module_meanrank_minavg = module_meanrank_minsum / (num_components - 1)
# We are interested if the features encapsulate gene modules
# A lower number across modules indicates a stronger ability to
# aggregate genes into features
module_min_rank = pd.DataFrame(module_meanrank_df.min(),
columns=['min_rank'])
return module_meanrank_df, module_min_rank, module_meanrank_minavg
def get_group_means(self, df):
"""
Get the mean latent space vector representation of input groups
"""
return df.assign(groups=self.other_df).groupby('groups').mean()
def get_subtraction(self, group_means, group_list):
"""
Subtract two group means given by group list
"""
a, b = group_list
a_df = group_means.loc[a, :]
b_df = group_means.loc[b, :]
subtraction = pd.DataFrame(a_df - b_df).T
return subtraction
def subtraction_essense(self, group_subtract, mean_rank, node):
"""
Obtain the difference between the subtraction and node of interest
"""
# subset mean rank to node of the "dropped" feature in the simulation
feature_essense = mean_rank.loc[:, node]
# The node essense is the compressed feature with the lowest mean rank
node_essense = feature_essense.idxmin()
node_idx = int(node_essense.split('_')[1])
# Ask how different this specific feature is from all others
group_z = zscore(group_subtract.iloc[0, :].tolist())
node_essense_zscore = group_z[node_idx]
return node_essense_zscore
def get_addition(self, group_means, subtraction, group):
"""
Add node to subtraction
"""
mean_feature = group_means.loc[group, :]
return subtraction + mean_feature
def reconstruct_group(self, lsa_result, algorithm=False):
"""
Reconstruct the latent space arithmetic result back to input dim
"""
if algorithm == 'tybalt':
return self.tybalt_fit.decoder.predict_on_batch(lsa_result)
elif algorithm == 'ctybalt':
return self.ctybalt_fit.decoder.predict_on_batch(lsa_result)
elif algorithm == 'adage':
return self.adage_fit.decoder.predict_on_batch(lsa_result)
elif algorithm == 'pca':
return self.pca_fit.inverse_transform(lsa_result)
elif algorithm == 'ica':
return self.ica_fit.inverse_transform(lsa_result)
elif algorithm == 'nmf':
return self.nmf_fit.inverse_transform(lsa_result)
else:
raise ValueError('algorithm must be one of: "pca", "ica", "nmf",' +
' "adage", "tybalt", or "ctybalt"')
def get_average_distance(self, transform_df, real_df):
"""
Obtain the average euclidean distance between the transformed vector
and all samples as part of the real dataframe
"""
return euclidean_distances(transform_df, real_df).mean()
def _wrap_sub_eval(self, weight_df, compress_df, num_components,
noise_column, subtraction_groups, addition_group, node,
real_df, algorithm):
"""
Helper function that wraps all evals
"""
# Get the module mean rank and the min rank sum
mean_rank_mod, mean_rank_min, min_rank_avg = (self.get_modules_ranks(
weight_df, num_components, noise_column))
# Begin subtraction analysis - first, get group means
group_means = self.get_group_means(compress_df)
# Next, get the subtraction result
sub_result = self.get_subtraction(group_means, subtraction_groups)
# Then, get the relative minimum difference to determine if the
# subtraction isolates the feature we expect is should - and how much
relative_min_diff = self.subtraction_essense(sub_result, mean_rank_mod,
node)
# Now reconstruct the subtraction back into original space
lsa_result = self.get_addition(group_means, sub_result, addition_group)
recon_lsa = self.reconstruct_group(lsa_result, algorithm)
avg_dist = self.get_average_distance(recon_lsa, real_df)
out_results = mean_rank_min.T
out_results.index = algorithm.split()
out_results = out_results.assign(minimum_rank_avg=min_rank_avg)
out_results = out_results.assign(min_node_zscore=relative_min_diff)
out_results = out_results.assign(avg_recon_dist=avg_dist)
return out_results
def subtraction_eval(self, num_components, noise_column, group_list,
add_groups, expect_node, real_df):
tybalt_results = self._wrap_sub_eval(weight_df=self.tybalt_weights,
compress_df=self.tybalt_df,
num_components=num_components,
noise_column=noise_column,
subtraction_groups=group_list,
addition_group=add_groups,
node=expect_node,
real_df=real_df,
algorithm='tybalt')
adage_results = self._wrap_sub_eval(weight_df=self.adage_weights,
compress_df=self.adage_df,
num_components=num_components,
noise_column=noise_column,
subtraction_groups=group_list,
addition_group=add_groups,
node=expect_node,
real_df=real_df,
algorithm='adage')
pca_results = self._wrap_sub_eval(weight_df=self.pca_weights,
compress_df=self.pca_df,
num_components=num_components,
noise_column=noise_column,
subtraction_groups=group_list,
addition_group=add_groups,
node=expect_node,
real_df=real_df,
algorithm='pca')
ica_results = self._wrap_sub_eval(weight_df=self.ica_weights,
compress_df=self.ica_df,
num_components=num_components,
noise_column=noise_column,
subtraction_groups=group_list,
addition_group=add_groups,
node=expect_node,
real_df=real_df,
algorithm='ica')
nmf_results = self._wrap_sub_eval(weight_df=self.nmf_weights,
compress_df=self.nmf_df,
num_components=num_components,
noise_column=noise_column,
subtraction_groups=group_list,
addition_group=add_groups,
node=expect_node,
real_df=real_df,
algorithm='nmf')
return pd.concat([
tybalt_results, adage_results, pca_results, ica_results,
nmf_results
])
class DataModel():
"""
Methods for loading and compressing input data
Usage:
from tybalt.data_models import DataModel
data = DataModel(filename)
"""
def __init__(self, filename=None, df=False, select_columns=False):
self.filename = filename
if filename is None:
self.df = df
else:
self.df = pd.read_table(self.filename)
if select_columns:
subset_df = self.df.iloc[:, select_columns]
other_columns = range(max(select_columns) + 1, self.df.shape[1])
self.other_df = self.df.iloc[:, other_columns]
self.df = subset_df
def transform(self, how):
self.transformation = how
if how == 'zscore':
self.transform_fit = StandardScaler().fit(self.df)
elif how == 'zeroone':
self.transform_fit = MinMaxScaler().fit(self.df)
else:
raise ValueError('how must be either "zscore" or "zeroone".')
self.df = pd.DataFrame(self.transform_fit.transform(self.df),
index=self.df.index,
columns=self.df.columns)
def pca(self, n_components, transform_df=False):
self.pca_fit = decomposition.PCA(n_components=n_components)
self.pca_df = self.pca_fit.fit_transform(self.df)
colnames = ['pca_{}'.format(x) for x in range(0, n_components)]
self.pca_df = pd.DataFrame(self.pca_df,
index=self.df.index,
columns=colnames)
if transform_df:
out_df = self.pca_fit.transform(transform_df)
return out_df
def ica(self, n_components, transform_df=False):
self.ica_fit = decomposition.FastICA(n_components=n_components)
self.ica_df = self.ica_fit.fit_transform(self.df)
colnames = ['ica_{}'.format(x) for x in range(0, n_components)]
self.ica_df = pd.DataFrame(self.ica_df,
index=self.df.index,
columns=colnames)
if transform_df:
out_df = self.ica_fit.transform(transform_df)
return out_df
def nmf(self, n_components, transform_df=False, init='nndsvdar', tol=5e-3):
self.nmf_fit = decomposition.NMF(n_components=n_components,
init=init,
tol=tol)
self.nmf_df = self.nmf_fit.fit_transform(self.df)
colnames = ['nmf_{}'.format(x) for x in range(0, n_components)]
self.nmf_df = pd.DataFrame(self.nmf_df,
index=self.df.index,
columns=colnames)
if transform_df:
out_df = self.nmf_fit.transform(transform_df)
return out_df
def nn(self, n_components, model='tybalt', transform_df=False, **kwargs):
# unpack kwargs
original_dim = kwargs.pop('original_dim', self.df.shape[1])
latent_dim = kwargs.pop('latent_dim', n_components)
batch_size = kwargs.pop('batch_size', 50)
epochs = kwargs.pop('epochs', 50)
learning_rate = kwargs.pop('learning_rate', 0.0005)
noise = kwargs.pop('noise', 0)
sparsity = kwargs.pop('sparsity', 0)
kappa = kwargs.pop('kappa', 1)
epsilon_std = kwargs.pop('epsilon_std', 1.0)
beta = kwargs.pop('beta', 0)
beta = K.variable(beta)
loss = kwargs.pop('loss', 'binary_crossentropy')
validation_ratio = kwargs.pop('validation_ratio', 0.1)
# Extra processing for conditional vae
if hasattr(self, 'other_df') and model == 'ctybalt':
y_df = kwargs.pop('y_df', self.other_df)
y_var = kwargs.pop('y_var', 'groups')
label_dim = kwargs.pop('label_dim', len(set(y_df[y_var])))
self.nn_train_y = y_df.drop(self.nn_test_df.index)
self.nn_test_y = y_df.drop(self.nn_train_df.index)
self.nn_train_y = self.nn_train_y.loc[self.nn_train_df.index, ]
self.nn_test_y = self.nn_test_y.loc[self.nn_test_df.index, ]
label_encoder = LabelEncoder().fit(self.other_df[y_var])
self.nn_train_y = label_encoder.transform(self.nn_train_y[y_var])
self.nn_test_y = label_encoder.transform(self.nn_test_y[y_var])
self.other_onehot = label_encoder.transform(self.other_df[y_var])
self.nn_train_y = to_categorical(self.nn_train_y)
self.nn_test_y = to_categorical(self.nn_test_y)
self.other_onehot = to_categorical(self.other_onehot)
self.nn_test_df = self.df.sample(frac=validation_ratio)
self.nn_train_df = self.df.drop(self.nn_test_df.index)
if model == 'tybalt':
self.tybalt_fit = Tybalt(original_dim=original_dim,
latent_dim=latent_dim,
batch_size=batch_size,
epochs=epochs,
learning_rate=learning_rate,
kappa=kappa,
epsilon_std=epsilon_std,
beta=beta,
loss=loss)
self.tybalt_fit.initialize_model()
self.tybalt_fit.train_vae(train_df=self.nn_train_df,
test_df=self.nn_test_df)
self.tybalt_weights = self.tybalt_fit.get_decoder_weights()
self.tybalt_df = self.tybalt_fit.compress(self.df)
colnames = ['vae_{}'.format(x) for x in range(0, latent_dim)]
self.tybalt_df.columns = colnames
if transform_df:
out_df = self.tybalt_fit.compress(transform_df)
return out_df
if model == 'ctybalt':
self.ctybalt_fit = cTybalt(original_dim=original_dim,
latent_dim=latent_dim,
label_dim=label_dim,
batch_size=batch_size,
epochs=epochs,
learning_rate=learning_rate,
kappa=kappa,
epsilon_std=epsilon_std,
beta=beta,
loss=loss)
self.ctybalt_fit.initialize_model()
self.ctybalt_fit.train_cvae(train_df=self.nn_train_df,
train_labels_df=self.nn_train_y,
test_df=self.nn_test_df,
test_labels_df=self.nn_test_y)
self.ctybalt_weights = self.ctybalt_fit.get_decoder_weights()
self.ctybalt_df = self.ctybalt_fit.compress(
[self.df, self.other_onehot])
colnames = ['cvae_{}'.format(x) for x in range(0, latent_dim)]
self.ctybalt_df.columns = colnames
if transform_df:
# Note: transform_df must be a list of two dfs [x_df, y_df]
out_df = self.ctybalt_fit.compress(transform_df)
return out_df
if model == 'adage':
self.adage_fit = Adage(original_dim=original_dim,
latent_dim=latent_dim,
noise=noise,
batch_size=batch_size,
epochs=epochs,
sparsity=sparsity,
learning_rate=learning_rate,
loss=loss)
self.adage_fit.initialize_model()
self.adage_fit.train_adage(train_df=self.nn_train_df,
test_df=self.nn_test_df)
self.adage_weights = self.adage_fit.get_decoder_weights()
self.adage_df = self.adage_fit.compress(self.df)
colnames = ['dae_{}'.format(x) for x in range(0, latent_dim)]
self.adage_df.columns = colnames
if transform_df:
out_df = self.adage_fit.compress(transform_df)
return out_df
def combine_models(self, include_labels=False, include_raw=False):
all_models = []
if hasattr(self, 'pca_df'):
all_models += [self.pca_df]
if hasattr(self, 'ica_df'):
all_models += [self.ica_df]
if hasattr(self, 'nmf_df'):
all_models += [self.nmf_df]
if hasattr(self, 'tybalt_df'):
all_models += [self.tybalt_df]
if hasattr(self, 'ctybalt_df'):
all_models += [self.ctybalt_df]
if hasattr(self, 'adage_df'):
all_models += [self.adage_df]
if include_raw:
all_models += [self.df]
if include_labels:
all_models += [self.other_df]
all_df = pd.concat(all_models, axis=1)
return all_df