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Chore: fix bug and typos #14

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360913e
FIX: typo
mei28 Feb 15, 2021
c2202e1
FIX: typos
mei28 Feb 15, 2021
6c90533
FIX: the problem of pos_color and negative_color always being reversed.
mei28 Feb 19, 2021
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2 changes: 1 addition & 1 deletion fairml/graphing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ def map_x(x):

fig = plt.figure(figsize=fig_size)

bar_colors = assign_colors_to_bars(coefficient_values, reverse=True)
bar_colors = assign_colors_to_bars(coefficient_values, reverse=reverse_values)
bar_colors = list(np.array(bar_colors)[index_sorted])

plt.barh(pos, sorted_column_values, align='center', color=bar_colors)
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2 changes: 1 addition & 1 deletion fairml/non_linear_expansion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ def return_non_linear_transformation(v1, poly, log, square_root,
A = np.append(A, current_power, axis=1)

if square_root:
sqrt = np.sqrt(v1 + nb.abs(min(v1)) + 1)
sqrt = np.sqrt(v1 + np.abs(min(v1)) + 1)
sqrt = np.reshape(sqrt, (sqrt.shape[0], 1))
A = np.append(A, sqrt, axis=1)

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20 changes: 10 additions & 10 deletions fairml/orthogonal_projection.py
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Expand Up @@ -45,7 +45,7 @@ def get_parallel_vector(v1, v2):
------------

v1 - baseline vector (numpy)
v2 - vector that you'd like to make parrallel to v1
v2 - vector that you'd like to make parallel to v1

Returns
-------------
Expand Down Expand Up @@ -118,7 +118,7 @@ def obtain_orthogonal_transformed_matrix(X, baseline_vector,


def audit_model(predict_function, input_dataframe, distance_metric="mse",
direct_input_pertubation_strategy="constant-zero",
direct_input_perturbation_strategy="constant-zero",
number_of_runs=10, include_interactions=False,
external_data_set=None):
"""
Expand All @@ -129,7 +129,7 @@ def audit_model(predict_function, input_dataframe, distance_metric="mse",
distance_metric -> one of ["mse", "accuracy"], this
variable defaults to regression.

direct_input_pertubation_strategy -> This is referring to how to zero out a
direct_input_perturbation_strategy -> This is referring to how to zero out a
single variable. One of three different options
1) replace with a random constant value
2) replace with median constant value
Expand Down Expand Up @@ -157,7 +157,7 @@ def audit_model(predict_function, input_dataframe, distance_metric="mse",
"dataframe")
assert distance_metric in ["mse", "accuracy"], ("Distance metric must be "
"'mse' or 'accuracy'")
assert direct_input_pertubation_strategy in ["constant-zero",
assert direct_input_perturbation_strategy in ["constant-zero",
"constant-median",
"random-sample"
], ("Perturbation strategy "
Expand All @@ -168,18 +168,18 @@ def audit_model(predict_function, input_dataframe, distance_metric="mse",

# either pass in a perturbation function for direct perturbation
# see perturbation functions
if not six.callable(direct_input_pertubation_strategy):
if not six.callable(direct_input_perturbation_strategy):
try:
_ = perturbation_strategy_dictionary[
direct_input_pertubation_strategy]
direct_input_perturbation_strategy]
except KeyError:
raise Exception("Invalid selection for direct_input_pertubation."
raise Exception("Invalid selection for direct_input_perturbation."
"Must be callable or one of "
"{}".format("-").join(
perturbation_strategy_dictionary.keys()))

# create output dictionaries
direct_pertubation_feature_output_dictionary = defaultdict(list)
direct_perturbation_feature_output_dictionary = defaultdict(list)
complete_perturbation_dictionary = defaultdict(list)

# interaction_perturbation_dictionary = defaultdict(list)
Expand Down Expand Up @@ -224,7 +224,7 @@ def audit_model(predict_function, input_dataframe, distance_metric="mse",
output_constant_col, normal_black_box_output)

# store independent output by themselves
direct_pertubation_feature_output_dictionary[
direct_perturbation_feature_output_dictionary[
list_of_column_names[col]].append(output_difference_col)

# now make all the remaining columns of the matrix
Expand Down Expand Up @@ -263,7 +263,7 @@ def audit_model(predict_function, input_dataframe, distance_metric="mse",
sign * complete_perturbation_dictionary[dictionary_key][i])

return (AuditResult(complete_perturbation_dictionary),
AuditResult(direct_pertubation_feature_output_dictionary))
AuditResult(direct_perturbation_feature_output_dictionary))


def main():
Expand Down