def visualize_result(self, result_dir, output_type, **kwargs):
info = read_table_file(os.path.join(result_dir, "files", "info.csv"))
info = info.set_index("filename")
classes = read_table_file(
os.path.join(result_dir, "files", "classes.csv"))
embeddings = ["PCA", "ICA", "Truncated SVD", "tSNE", "UMAP"]
if output_type == "pdf":
from plotly import graph_objs as go, io as pio
for class_label in classes.index.values:
class_names = classes.loc[class_label].astype("str").values
for embedding_name in embeddings:
embedding_slug = embedding_name.replace(" ", "_").lower()
filename = "%s_%s.csv" % (class_label, embedding_slug)
embedded_df = read_table_file(
os.path.join(result_dir, "files", filename))
fig = go.Figure(layout=go.Layout(title=info.loc[filename]
["plot_description"],
font=dict(size=8)))
color_scale = self._get_color_scales(class_names)
clusters = embedded_df["k_means_clusters"].values
X = embedded_df["X"].values
Y = embedded_df["Y"].values
for i, class_name in enumerate(
list(sorted(set(class_names)))):
indices = [
j for j, c in enumerate(class_names)
if c == class_name
]
color = color_scale[i]
fig.add_scatter(x=X[indices],
y=Y[indices],
mode='markers',
marker=dict(
color=color,
opacity=0.5,
symbol=[
ployly_symbols[c]
for c in clusters[indices]
]),
name=class_name)
pio.write_image(fig,
os.path.join(
result_dir, "plot_%s_%s.pdf" %
(class_label, embedding_slug)),
width=800,
height=600)
elif output_type == "html":
raise NotImplementedError()
else:
raise NotImplementedError()
def visualize_result(self, result_dir, output_type, **kwargs):
info = read_table_file(os.path.join(result_dir, "files", "info.csv"))
info = info.set_index("filename")
spearman_correlations = read_table_file(
os.path.join(result_dir, "files", "spearman_correlations.csv"))
pearson_correlations = read_table_file(
os.path.join(result_dir, "files", "pearson_correlations.csv"))
n = spearman_correlations.shape[0] // 2
adt_adt_spearmanr = spearman_correlations.iloc[:n, :n]
rna_rna_spearmanr = spearman_correlations.iloc[n:, n:]
adt_rna_spearmanr = spearman_correlations.iloc[:n, n:]
adt_adt_pearsonr = pearson_correlations.iloc[:n, :n]
rna_rna_pearsonr = pearson_correlations.iloc[n:, n:]
adt_rna_pearsonr = pearson_correlations.iloc[:n, n:]
if output_type == "pdf":
import plotly.graph_objs as go
import plotly.io as pio
plots = [
("Pairwise Spearman correlations between ADT values",
adt_adt_spearmanr, "heatmap_adt_adt_spearmanr.pdf"),
("Pairwise Spearman correlations between RNA values",
rna_rna_spearmanr, "heatmap_rna_rna_spearmanr.pdf"),
("Pairwise Spearman correlations between ADT and RNA values",
adt_rna_spearmanr, "heatmap_adt_rna_spearmanr.pdf"),
("Pairwise Pearson correlations between ADT values",
adt_adt_pearsonr, "heatmap_adt_adt_pearsonr.pdf"),
("Pairwise Pearson correlations between RNA values",
rna_rna_pearsonr, "heatmap_rna_rna_pearsonr.pdf"),
("Pairwise Pearson correlations between ADT and RNA values",
adt_rna_pearsonr, "heatmap_adt_rna_pearsonr.pdf")
]
for title, data_frame, filename in plots:
fig = go.Figure(
layout=go.Layout(title=title, font=dict(size=9)))
fig.add_heatmap(z=data_frame.values,
x=data_frame.columns.values,
y=data_frame.index.values,
colorscale='Picnic') # RdBu is also good
pio.write_image(fig,
os.path.join(result_dir, filename),
width=600,
height=700)
elif output_type == "html":
print("Nothing to visualize")
else:
raise NotImplementedError()
def _load_data_and_imputed_data_for_evaluation(self, processed_count_file):
hidden_data_file_path = os.path.join(settings.STORAGE_DIR,
"%s.hidden.pkl.gz" % self.uid)
sparse_data, original_columns, column_permutation = load_gzip_pickle(
hidden_data_file_path)
data = sparse_data.to_dense()
del sparse_data
imputed_data = read_table_file(processed_count_file)
# Restoring original column names
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Remove (error correction) ERCC and mitochondrial RNAs
remove_list = [
symbol for symbol in imputed_data.index.values
if symbol.startswith("ERCC-") or symbol.startswith("mt-")
]
imputed_data = imputed_data.drop(remove_list)
data = data.drop(remove_list)
return data, imputed_data
def visualize_result(self, result_dir, output_type, **kwargs):
info = read_table_file(os.path.join(result_dir, "files", "info.csv"))
info = info.set_index("filename")
predictions = read_table_file(
os.path.join(result_dir, "files", "predictions.csv"))
original_values = predictions["original"]
predicted_values = predictions["predicted"]
if output_type == "pdf":
import plotly.graph_objs as go
import plotly.io as pio
max_axis = float(max(original_values.max(),
predicted_values.max()))
for transformation_name in ["log", "sqrt"]:
transformation = transformations[transformation_name]
fig = go.Figure(layout=go.Layout(
title=
'Predicted values vs. original masked values (%s scale)' %
transformation_name,
font=dict(size=12),
xaxis=go.layout.XAxis(
range=[0, transformation(max_axis)]),
yaxis=go.layout.YAxis(
range=[0, transformation(max_axis)])))
fig.add_scatter(x=transformation(original_values),
y=transformation(predicted_values),
mode='markers',
marker=dict(opacity=0.3))
pio.write_image(
fig,
os.path.join(
result_dir,
"prediction_plot_%s_scale.pdf" % transformation_name),
width=800,
height=800)
elif output_type == "html":
raise NotImplementedError()
else:
raise NotImplementedError()
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
normalization = kwargs['normalization']
transformation = kwargs['transformation']
clear_cache = kwargs['clear_cache']
make_sure_dir_exists(os.path.join(result_dir, "files"))
info = []
# Load hidden state and data
count_matrix, classes, original_columns, column_permutation = self._load_hidden_state(
)
# Load imputed data
imputed_data = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Data transformations
if np.sum(imputed_data.values < 0) > 0:
log("Observed some negative values!")
imputed_data[imputed_data < 0] = 0
imputed_data = transformations[transformation](
normalizations[normalization](imputed_data))
# Save class details for future
write_csv(classes, os.path.join(result_dir, "files", "classes.csv"))
# Evaluation
metric_results = dict()
embedded_data_file_path = os.path.join(result_dir, "files",
"embedded_data.pkl.gz")
if os.path.exists(embedded_data_file_path) and not clear_cache:
embedded_data = load_gzip_pickle(embedded_data_file_path)
else:
embedded_data = self._get_embeddings(imputed_data)
dump_gzip_pickle(embedded_data, embedded_data_file_path)
log("Evaluating ...")
for class_label in classes.index.values:
class_names = classes.loc[class_label].values
for embedding_name in embedded_data:
emb, emb_2d = embedded_data[embedding_name]
embedding_slug = embedding_name.replace(" ", "_").lower()
k_means = KMeans(n_clusters=len(set(class_names)))
k_means.fit(emb)
clusters = k_means.predict(emb)
embedding_df = pd.DataFrame(emb)
embedding_df["X"] = emb_2d[:, 0]
embedding_df["Y"] = emb_2d[:, 1]
embedding_df["class"] = class_names
embedding_df["k_means_clusters"] = clusters
write_csv(
embedding_df,
os.path.join(result_dir, "files",
"%s_%s.csv" % (class_label, embedding_slug)))
info.append({
'filename':
"%s_%s.csv" % (class_label, embedding_slug),
'description':
'%s embedding of cells along %s labels' %
(embedding_name, class_label),
'plot_description':
'%s embedding of cells along %s labels (Classes can be identified '
'with their colors and K-means clusters are marked '
'with different shapes)' % (embedding_name, class_label),
})
metric_results.update({
'kmeans_on_%s_%s_adjusted_mutual_info_score' % (embedding_slug, class_label):
adjusted_mutual_info_score(class_names,
clusters,
average_method="arithmetic"),
'kmeans_on_%s_%s_v_measure_score' % (embedding_slug, class_label):
v_measure_score(class_names, clusters),
'embedding_%s_%s_calinski_harabaz_score' % (embedding_slug, class_label):
calinski_harabaz_score(emb, class_names),
'embedding_%s_%s_silhouette_score' % (embedding_slug, class_label):
silhouette_score(emb, class_names)
})
write_csv(pd.DataFrame(info),
os.path.join(result_dir, "files", "info.csv"))
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" % (metric, metric_results[metric]))
file.write("##\n## ADDITIONAL INFO:\n")
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
normalization = kwargs['normalization']
transformation = kwargs['transformation']
# Load hidden state and data
count_matrix_lq, original_columns, column_permutation, count_matrix_hq = self._load_hidden_state(
)
# Load imputed data
imputed_data = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Replace negative values with zero
imputed_data = imputed_data.clip(lower=0)
# Data transformations
imputed_data = transformations[transformation](
normalizations[normalization](imputed_data))
count_matrix_hq = transformations[transformation](
normalizations[normalization](count_matrix_hq))
# Evaluation
rmse_distances = []
mae_distances = []
euclidean_distances = []
cosine_distances = []
correlation_distances = []
for i in range(count_matrix_hq.shape[1]):
non_zeros = np.logical_and(count_matrix_hq.values[:, i] > 0,
count_matrix_lq.values[:, i] == 0)
hq = count_matrix_hq.values[non_zeros, i]
lq = count_matrix_lq.values[non_zeros, i]
y = imputed_data.values[non_zeros, i]
if np.sum(y) > 0:
y = y * np.sum(hq) / np.sum(y)
rmse_distances.append(float(np.mean(np.square(hq - y)**0.5)))
mae_distances.append(float(np.mean(np.abs(hq - y))))
euclidean_distances.append(
pdist(np.vstack((hq, y)), 'euclidean')[0])
cosine_distances.append(pdist(np.vstack((hq, y)), 'cosine')[0])
correlation_distances.append(
pdist(np.vstack((hq, y)), 'correlation')[0])
metric_results = {
'cell_root_mean_squared_error': np.mean(rmse_distances),
'cell_mean_absolute_error': np.mean(mae_distances),
'cell_mean_euclidean_distance': np.mean(euclidean_distances),
'cell_mean_cosine_distance': np.mean(cosine_distances),
'cell_mean_correlation_distance': np.mean(correlation_distances)
}
# Save results to a file
make_sure_dir_exists(os.path.join(result_dir, "files"))
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" %
(metric, float(metric_results[metric])))
file.write("##\n## ADDITIONAL INFO:\n")
file.write(
"# CELL\troot_mean_squared_error\tmean_absolute_error\tmean_euclidean_distance\t"
"mean_cosine_distance\tmean_correlation_distance:\n")
for i in range(count_matrix_hq.shape[1]):
file.write(
"# %s\t%f\t%f\t%f\t%f\t%f\n" %
(count_matrix_hq.columns.values[i], rmse_distances[i],
mae_distances[i], euclidean_distances[i],
cosine_distances[i], correlation_distances[i]))
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
make_sure_dir_exists(os.path.join(result_dir, "files"))
transformation = kwargs['transformation']
# Load hidden state and data
_, original_columns, column_permutation, count_adt, protein_rna_mapping = self._load_hidden_state(
)
# Load imputed data
imputed_rna = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_rna = rearrange_and_rename_columns(imputed_rna,
original_columns,
column_permutation)
# Data transformations
imputed_rna = transformations[transformation](imputed_rna)
count_adt = transformations[transformation](count_adt)
# Use related data
adt = count_adt.loc[[
prot for prot in count_adt.index.values
if (protein_rna_mapping[prot] in imputed_rna.index.values)
]].copy()
adt.index = ["prot_" + p for p in adt.index.values]
rna = imputed_rna.loc[[
protein_rna_mapping[prot] for prot in count_adt.index.values
if (protein_rna_mapping[prot] in imputed_rna.index.values)
]]
rna.index = ["gene_" + g for g in rna.index.values]
info = []
write_csv(adt, os.path.join(result_dir, "files", "adt.csv"))
info.append({
'filename':
"adt.csv",
'description':
'Protein expressions (adt) after transformation',
'plot_description':
'Protein expressions (adt) after transformation',
})
write_csv(rna, os.path.join(result_dir, "files", "rna.csv"))
info.append({
'filename':
"rna.csv",
'description':
'Gene expressions of genes related to adt data after transformation',
'plot_description':
'Gene expressions of genes related to adt data after transformation',
})
n = adt.shape[0]
# Calculating Spearman correlations
combined_df = pd.concat((adt, rna)).transpose()
correlations = combined_df.corr(method="spearman")
adt_adt_spearmanr = correlations.iloc[:n, :n]
rna_rna_spearmanr = correlations.iloc[n:, n:]
adt_rna_spearmanr = correlations.iloc[:n, n:]
write_csv(
correlations,
os.path.join(result_dir, "files", "spearman_correlations.csv"))
info.append({
'filename':
"spearman_correlations.csv",
'description':
'Pairwise Spearman correlations (first n items are '
'adt expressions and second n items are rna expressions)',
'plot_description':
'Pairwise Spearman correlations (first n items are '
'adt expressions and second n items are rna expressions)',
})
# Calculating Pearson correlations
combined_df = pd.concat((adt, rna)).transpose()
correlations = combined_df.corr(method="pearson")
adt_adt_pearsonr = correlations.iloc[:n, :n]
rna_rna_pearsonr = correlations.iloc[n:, n:]
adt_rna_pearsonr = correlations.iloc[:n, n:]
write_csv(
correlations,
os.path.join(result_dir, "files", "pearson_correlations.csv"))
info.append({
'filename':
"pearson_correlations.csv",
'description':
'Pairwise Pearson correlations (first n items are '
'adt expressions and second n items are rna expressions)',
'plot_description':
'Pairwise Pearson correlations (first n items are '
'adt expressions and second n items are rna expressions)',
})
# Evaluation
metric_results = {
'rna_protein_mean_spearman_correlatoin':
np.mean(adt_rna_spearmanr.values.diagonal()),
'rna_protein_mean_pearson_correlatoin':
np.mean(adt_rna_pearsonr.values.diagonal()),
'MSE_of_adt_adt_and_rna_rna_spearman_correlations':
np.mean((adt_adt_spearmanr.values - rna_rna_spearmanr.values)**2),
'MSE_of_adt_adt_and_rna_rna_pearson_correlations':
np.mean((adt_adt_pearsonr.values - rna_rna_pearsonr.values)**2)
}
write_csv(pd.DataFrame(info),
os.path.join(result_dir, "files", "info.csv"))
# Save results to a file
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" %
(metric, float(metric_results[metric])))
file.write("##\n## ADDITIONAL INFO:\n")
file.write("## Pearson of adt/rna:\n")
file.write("## " +
"\n## ".join(adt_rna_pearsonr.to_string().split("\n")) +
"\n")
file.write('## Spearman of adt/rna:\n')
file.write(
"## " +
"\n## ".join(adt_rna_spearmanr.to_string().split("\n")) + "\n")
file.write("## Pearson of adt/adt:\n")
file.write("## " +
"\n## ".join(adt_adt_pearsonr.to_string().split("\n")) +
"\n")
file.write("## Pearson of rna/rna:\n")
file.write("## " +
"\n## ".join(rna_rna_pearsonr.to_string().split("\n")) +
"\n")
file.write('## Spearman of adt/adt:\n')
file.write(
"## " +
"\n## ".join(adt_adt_spearmanr.to_string().split("\n")) + "\n")
file.write('## Spearman of rna/rna:\n')
file.write(
"## " +
"\n## ".join(rna_rna_spearmanr.to_string().split("\n")) + "\n")
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results
def visualize_result(self, result_dir, output_type, **kwargs):
info = read_table_file(os.path.join(result_dir, "files", "info.csv"))
info = info.set_index("filename")
G1_S_related_part_of_imputed_data = read_table_file(
os.path.join(result_dir, "files",
"G1_S_related_part_of_imputed_data.csv"))
G2_M_related_part_of_imputed_data = read_table_file(
os.path.join(result_dir, "files",
"G2_M_related_part_of_imputed_data.csv"))
embeddings = ["PCA", "ICA", "Truncated SVD", "tSNE", "UMAP"]
embedded_dfs = dict()
for embedding_name in embeddings:
embedding_slug = embedding_name.replace(" ", "_").lower()
embedded_dfs[embedding_name] = read_table_file(
os.path.join(result_dir, "files", "%s.csv" % embedding_slug))
if output_type == "pdf":
import plotly.graph_objs as go
import plotly.io as pio
G1_S_heatmap_fig = go.Figure(layout=go.Layout(
title='Heatmap of Genes related to G1/S',
font=dict(size=5),
xaxis=dict(title='Marker Genes', tickangle=60)))
G2_M_heatmap_fig = go.Figure(layout=go.Layout(
title='Heatmap of Genes related to G2/M',
font=dict(size=5),
xaxis=dict(title='Marker Genes', tickangle=60)))
def normalize(df):
return df.subtract(df.mean(axis=1),
axis=0).divide(df.std(axis=1), axis=0)
G1_S_heatmap_fig.add_heatmap(
z=normalize(G1_S_related_part_of_imputed_data).values.T,
x=G1_S_related_part_of_imputed_data.index.values,
y=G1_S_related_part_of_imputed_data.columns.values,
colorscale='Viridis')
G2_M_heatmap_fig.add_heatmap(
z=normalize(G2_M_related_part_of_imputed_data).values.T,
x=G2_M_related_part_of_imputed_data.index.values,
y=G2_M_related_part_of_imputed_data.columns.values,
colorscale='Viridis')
pio.write_image(G1_S_heatmap_fig,
os.path.join(
result_dir,
"plot_G1_S_related_genes_heatmap.pdf"),
width=600,
height=700)
pio.write_image(G2_M_heatmap_fig,
os.path.join(
result_dir,
"plot_G2_M_related_genes_heatmap.pdf"),
width=600,
height=700)
embeddings = ["PCA", "ICA", "Truncated SVD", "tSNE", "UMAP"]
for i, embedding_name in enumerate(embeddings):
embedding_slug = embedding_name.replace(" ", "_").lower()
fig = go.Figure(layout=go.Layout(
title='%s embedding of cells considering genes related '
'to cell-cycle (K-means clusters are marked '
'with different shapes)' % embedding_name,
font=dict(size=8)))
embedding_df = embedded_dfs[embedding_name]
X = embedding_df["X"].values
Y = embedding_df["Y"].values
classes = embedding_df["class"].values
clusters = embedding_df["k_means_clusters"].values
for j, state in enumerate(["G1", "G2M", "S"]):
indices = [k for k, c in enumerate(classes) if c == state]
fig.add_scatter(x=X[indices],
y=Y[indices],
mode='markers',
marker=dict(color=["red", "green",
"blue"][j],
symbol=[[
"circle-open", "diamond",
"cross"
][c] for c in clusters[indices]
]),
name="%s Phase" % state)
pio.write_image(fig,
os.path.join(result_dir,
"plot_%s.pdf" % embedding_slug),
width=800,
height=600)
elif output_type == "html":
raise NotImplementedError()
else:
raise NotImplementedError()
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
transformation = kwargs['transformation']
make_sure_dir_exists(os.path.join(result_dir, "files"))
# Load hidden state and data
scaled_data, original_columns, column_permutation = self._load_hidden_state(
)
# Load imputed data
imputed_data = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Replace negative values with zero
imputed_data = imputed_data.clip(lower=0)
# Data transformation
scaled_data = transformations[transformation](scaled_data)
imputed_data = transformations[transformation](imputed_data)
# Evaluation
rmse_distances = []
mae_distances = []
euclidean_distances = []
cosine_distances = []
correlation_distances = []
rmse = float(
np.sum(
np.where(scaled_data.values > 0, 1, 0) *
np.square(scaled_data.values - imputed_data.values)) /
np.sum(np.where(scaled_data.values > 0, 1, 0)))**0.5
mae = float(
np.sum(
np.where(scaled_data.values > 0, 1, 0) *
np.abs(scaled_data.values - imputed_data.values)) /
np.sum(np.where(scaled_data.values > 0, 1, 0)))
for i in range(scaled_data.shape[1]):
non_zeros = scaled_data.values[:, i] > 0
x = scaled_data.values[non_zeros, i]
y = imputed_data.values[non_zeros, i]
rmse_distances.append(
float(np.sum(np.square(x - y)) / np.sum(non_zeros))**0.5)
mae_distances.append(
float(np.sum(np.abs(x - y)) / np.sum(non_zeros)))
cosine_distances.append(pdist(np.vstack((x, y)), 'cosine')[0])
euclidean_distances.append(
pdist(np.vstack((x, y)), 'euclidean')[0])
correlation_distances.append(
pdist(np.vstack((x, y)), 'correlation')[0])
metric_results = {
'all_mean_absolute_error_on_non_zeros':
mae,
'all_root_mean_squared_error_on_non_zeros':
rmse,
'cell_mean_mean_absolute_error_on_non_zeros':
np.mean(mae_distances),
'cell_mean_root_mean_squared_error_on_non_zeros':
np.mean(rmse_distances),
'cell_mean_euclidean_distance':
np.mean(euclidean_distances),
'cell_mean_cosine_distance':
np.mean(cosine_distances),
'cell_mean_correlation_distance':
np.mean(correlation_distances),
}
# Save results to a file
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" %
(metric, float(metric_results[metric])))
file.write("##\n## ADDITIONAL INFO:\n")
file.write(
"# CELL\troot_mean_squared_error_on_non_zeros\tmean_absolute_error_on_non_zeros\t"
"euclidean_distance_on_non_zeros\tcosine_distance_on_non_zeros\tcorrelation_distance_on_non_zeros:\n"
)
for i in range(scaled_data.shape[1]):
file.write("# %s\t%f\t%f\t%f\t%f\t%f\n" %
(scaled_data.columns.values[i], rmse_distances[i],
mae_distances[i], euclidean_distances[i],
cosine_distances[i], correlation_distances[i]))
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results
def evaluate_result(self, processed_count_file_path, result_dir,
visualization, **kwargs):
make_sure_dir_exists(os.path.join(result_dir, "files"))
info = []
# Load hidden state and data
data, mask, original_columns, column_permutation = self._load_hidden_state(
)
# Load imputed data
imputed_data = read_table_file(processed_count_file_path)
# Restore column names and order
imputed_data = rearrange_and_rename_columns(imputed_data,
original_columns,
column_permutation)
# Replace negative values with zero
imputed_data = imputed_data.clip(lower=0)
# Evaluation
log_diff = np.abs(transformations["log"](data) -
transformations["log"](imputed_data))
sqrt_diff = np.abs(transformations["sqrt"](data) -
transformations["sqrt"](imputed_data))
mse_on_log = float(
np.sum(
np.sum(mask * np.where(data != 0, 1, 0) * np.square(log_diff)))
/ np.sum(np.sum(mask * np.where(data != 0, 1, 0))))
mae_on_log = float(
np.sum(np.sum(mask * np.where(data != 0, 1, 0) * np.abs(log_diff)))
/ np.sum(np.sum(mask * np.where(data != 0, 1, 0))))
mse_on_sqrt = float(
np.sum(
np.sum(
mask * np.where(data != 0, 1, 0) * np.square(sqrt_diff))) /
np.sum(np.sum(mask * np.where(data != 0, 1, 0))))
mae_on_sqrt = float(
np.sum(np.sum(
mask * np.where(data != 0, 1, 0) * np.abs(sqrt_diff))) /
np.sum(np.sum(mask * np.where(data != 0, 1, 0))))
metric_results = {
'RMSE_sqrt': mse_on_sqrt**0.5,
'MAE_sqrt': mae_on_sqrt,
'RMSE_log': mse_on_log**0.5,
'MAE_log': mae_on_log
}
masked_locations = []
mask_values = mask.values
for x in range(mask_values.shape[0]):
for y in range(mask_values.shape[1]):
if mask_values[x, y] == 1:
masked_locations.append((x, y))
original_values = []
predicted_values = []
for (x, y) in masked_locations:
original_values.append(data.iloc[x, y])
predicted_values.append(imputed_data.iloc[x, y])
original_values = np.asarray(original_values)
predicted_values = np.asarray(predicted_values)
predictions_df = pd.DataFrame({
'original': original_values,
'predicted': predicted_values
})
write_csv(predictions_df,
os.path.join(result_dir, "files", "predictions.csv"))
info.append({
'filename':
"predictions.csv",
'description':
'Original masked values along predicted values',
'plot_description':
'Predicted values vs. original masked values',
})
write_csv(pd.DataFrame(info),
os.path.join(result_dir, "files", "info.csv"))
# Save results to a file
result_path = os.path.join(result_dir, "result.txt")
with open(result_path, 'w') as file:
file.write("## METRICS:\n")
for metric in sorted(metric_results):
file.write("%s\t%4f\n" % (metric, metric_results[metric]))
file.write("##\n## ADDITIONAL INFO:\n")
file.write("# GENE\tCELL\tGOLD_STANDARD\tRESULT:\n")
for (x, y) in masked_locations:
file.write("# %s\t%s\t%f\t%f\n" %
(data.index.values[x], data.columns.values[y],
data.iloc[x, y], imputed_data.iloc[x, y]))
log("Evaluation results saved to `%s`" % result_path)
if visualization != "none":
self.visualize_result(result_dir, output_type=visualization)
return metric_results