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Merged
ddomingof merged 2 commits into from
May 12, 2020
Merged

match_delete_rows in format_input #24

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3e4debf
match_delete_rows in format_input, solve dimensional errors in cross …
jmarinllao May 12, 2020
6998fc2
flake8 corrections
jmarinllao May 12, 2020
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2 changes: 1 addition & 1 deletion src/diffupy/cli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,7 +165,7 @@ def diffuse(
threshold,
)

click.secho(f'Computing the diffusion algorithm.')
click.secho('Computing the diffusion algorithm.')

results = run_diffusion(
input_scores_dict,
Expand Down
16 changes: 9 additions & 7 deletions src/diffupy/process_input.py
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Original file line number Diff line number Diff line change
Expand Up @@ -199,8 +199,8 @@ def _load_data_input_from_file(path: str, **further_parse_args) -> Union[pd.Data

else:
raise IOError(
f'There is a problem with your file. Please ensure the file you submitted is correctly formatted with a'
f'.csv or .tsv file extension.'
'There is a problem with your file. Please ensure the file you submitted is correctly formatted with a'
'.csv or .tsv file extension.'
)


Expand Down Expand Up @@ -231,9 +231,9 @@ def _codify_input_data(

# Standardize the title of the node column labeling column to 'Label', for later processing.
if LABEL not in df.columns:
for l in list(df.columns):
if l in NODE_LABELING:
df = df.rename(columns={l: LABEL})
for column_label in list(df.columns):
if column_label in NODE_LABELING:
df = df.rename(columns={column_label: LABEL})
break

# If node type provided in a column, classify in a dictionary the input codification by its node type.
Expand Down Expand Up @@ -852,7 +852,8 @@ def format_categorical_input_vector_from_label_list(
)
)

return input_mat.match_missing_rows(kernel.rows_labels, missing_value).match_rows(kernel)
return input_mat.match_delete_rows(kernel.rows_labels).match_missing_rows(kernel.rows_labels,
missing_value).match_rows(kernel)


def format_categorical_input_matrix_from_label_list(
Expand Down Expand Up @@ -935,7 +936,8 @@ def format_input_vector_from_label_score_dict(
)
)

return input_mat.match_missing_rows(kernel.rows_labels, missing_value).match_rows(kernel)
return input_mat.match_delete_rows(kernel.rows_labels).match_missing_rows(kernel.rows_labels,
missing_value).match_rows(kernel)


def format_input_matrix_from_type_label_score_dict(
Expand Down