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import os
import pickle
import random
import dice_ml
import gradio as gr
import pandas as pd
from sklearn.model_selection import train_test_split
def read_csv(dataset):
path = './data/' + dataset + '.csv'
data = pd.read_csv(path)
return data.head(10)
def train_model(input_df, target, test_size, model_name, features_to_drop):
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
path = './data/' + input_df + '.csv'
data = pd.read_csv(path, index_col=0)
data = data.dropna()
data = data.drop(features_to_drop, axis=1)
metrics = ['TSV','TPV','TCV','TSL']
metrics.remove(target[0])
data = data.drop(metrics, axis=1)
features = data.drop(target, axis=1).columns.to_list()
target_f = data[target[0]]
datasetX = data.drop(target, axis=1)
X_train, X_test, y_train, y_test = train_test_split(datasetX, target_f,
test_size=test_size, random_state=42)
categorical_features = X_train.columns.difference(features)
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_transformer = Pipeline(steps=[
('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore'))])
transformations = ColumnTransformer(
transformers=[
('num', numeric_transformer, features),
('cat', categorical_transformer, categorical_features)])
regr = Pipeline(steps=[('preprocessor', transformations),
('regressor', RandomForestRegressor())])
model = regr.fit(X_train, y_train)
pickle.dump(model, open('./' + model_name + '.pkl', 'wb'))
return 'Model Saved'
def generate_cfs_total(input_df_T, target_T, radio_T, predefined_T, custom_T, dropped_features_T, freeze_features_T, model_T):
path = './data/' + input_df_T + '.csv'
data = pd.read_csv(path)
data = data.dropna()
model = pickle.load(open('./' + model_T + '.pkl', 'rb'))
data = data.drop(dropped_features_T, axis=1)
metrics = ['TSV','TPV','TCV','TSL']
metrics.remove(target_T[0])
data = data.drop(metrics, axis=1)
features = data.drop(target_T[0], axis=1).columns.tolist()
target = data[target_T[0]]
datasetX = data.drop('TSV', axis=1)
x_train, x_test, y_train, y_test = train_test_split(datasetX,
target,
test_size=0.2,
random_state=0)
always_immutable = ['AvgMaxDailyTemp','AvgMinDailyTemp','School','DAY','StartTime']
freezed = always_immutable + freeze_features_T + [target_T[0]]
features_to_vary = data.columns.difference(freezed).to_list()
d = dice_ml.Data(dataframe=data, continuous_features=features, outcome_name=target_T[0])
m = dice_ml.Model(model=model, backend='sklearn', model_type='regressor')
exp = dice_ml.Dice(d, m, method='random')
if radio_T == 'Predefined':
random_index = random.randint(0, len(x_train-2))
print(int(predefined_T))
query_instances = x_test[random_index:random_index+int(predefined_T)]
elif radio_T == 'Custom':
query_instances = custom_T
dice_exp = exp.generate_counterfactuals(query_instances, total_CFs=4, desired_range=[0.0, 2.0], features_to_vary=features_to_vary)
return dice_exp.visualize_as_dataframe(show_only_changes=True)
def generate_cfs_individual(input_df_I, target_I, radio_I, predefined_I, custom_I, dropped_features_I, freeze_features_I, model_I):
pass
with gr.Blocks() as demo:
with gr.Tab('Dataset'):
gr.Markdown('Visualize the dataset to apply CFML')
df_vis = gr.Dropdown(['IndividualClothingBinary','IndividualClothingBinary+3Binary',
'IndividualClothingValue','IndividualClothingValue+3Binary','Multi_TotalCLO_w_Chair',
'Summer','TotalClothingValue','TotalClothingValue+3Binary'], label='Dataset')
output_vis = gr.DataFrame()
button_vis = gr.Button(label="Run")
with gr.Tab('Model'):
gr.Markdown('Choose the features to apply CFML')
input_df = gr.Dropdown(['IndividualClothingBinary','IndividualClothingBinary+3Binary',
'IndividualClothingValue','IndividualClothingValue+3Binary','Multi_TotalCLO_w_Chair',
'Summer','TotalClothingValue','TotalClothingValue+3Binary'], label='Dataset')
target = gr.CheckboxGroup(['TSV','TPV','TCV','TSL'], label='Target Metric', info='Please select only one')
test_size = gr.Slider(minimum=0.1, maximum=0.5, step=0.05, value=0.2, label='Test Size', interactive=True)
model_name = gr.Textbox(label='Model Name', placeholder='Enter the model name')
features_to_drop = gr.CheckboxGroup(['SwC', 'MC', 'Grade', 'Age', 'Gender'],
label='Features to Drop', info='Select the features to drop')
model_output = gr.Textbox(label='Status')
button_model = gr.Button(label="Train Model")
#list add .pkl files from models folder
models = []
for file in os.listdir('./'):
if file.endswith('.pkl'):
models.append(file.split('.')[0])
with gr.Tab('Counterfactuals-Total'):
gr.Markdown('Generate Counterfactuals for Total CLO Dataset')
input_df_T = gr.Dropdown(['Multi_TotalCLO_w_Chair','Summer',
'TotalClothingValue','TotalClothingValue+3Binary'], label='Dataset')
target_T = gr.CheckboxGroup(['TSV','TPV','TCV','TSL'], label='Target Metric', info='Please select only one')
#target_T_range = gr.Textbox(label='Target Range', placeholder='Enter the target range [start,end]')
radio_T = gr.Radio(['Predefined', 'Custom'], label='Type of Input')
predefined_T = gr.Number(default=0, label='Number of inputs to provide')
custom_T = gr.Dataframe(
headers=['DAY','School','SchoolType','StartTime','AvgMaxDailyTemp','AvgMinDailyTemp','AvgIndoorRelativeHumidity',
'IndoorTempDuringSurvey','Grade','Age','Gender','FormalClothing','TotalCLOwithChair'],
row_count=(2, 'dynamic')
)
dropped_features_T = gr.CheckboxGroup(['SwC', 'MC', 'Grade', 'Age', 'Gender'],
label='Features to Drop', info='Select the features that are dropped from feature set')
freeze_features_T = gr.CheckboxGroup(['AvgIndoorRelativeHumidity',
'IndoorTempDuringSurvey','Grade','Age','Gender','FormalClothing','TotalCLOwithChair'],
info = 'Select the features to be freezed to generate CFs')
model_T = gr.Dropdown(models, label='Model', info='Select the model to generate CFs')
output_T = gr.DataFrame()
button_cf_T = gr.Button(label="Generate CFs")
with gr.Tab('Counterfactuals-Individual'):
gr.Markdown('Generate Counterfactuals for Individual Clothing Dataset')
input_df_I = gr.Dropdown(['IndividualClothingBinary','IndividualClothingBinary+3Binary',
'IndividualClothingValue','IndividualClothingValue+3Binary'], label='Dataset')
target_I = gr.CheckboxGroup(['TSV','TPV','TCV','TSL'], label='Target Metric', info='Please select only one')
radio_I = gr.Radio(['Predefined', 'Custom'], label='Type of Input')
predefined_I = gr.Number(default=0, label='Number of inputs to provide')
custom_I = gr.Dataframe(
headers=['DAY','School','SchoolType','StartTime','AvgMaxDailyTemp','AvgMinDailyTemp','AvgIndoorRelativeHumidity','IndoorTempDuringSurvey',
'Grade','Age','Gender','FormalClothing','Pant','Trackpant','Halfshirt','Blazer','Jacket','Skirt',
'FullShirt','HalfSweater','Tshirt','Socks','Thermal','Vest','FullSweater','SwC','MC'],
row_count=(2, 'dynamic')
)
dropped_features_I = gr.CheckboxGroup(['SwC', 'MC', 'Grade', 'Age', 'Gender'],
label='Features to Drop', info='Select the features that are dropped from feature set')
freeze_features_I = gr.CheckboxGroup(['AvgIndoorRelativeHumidity','IndoorTempDuringSurvey',
'Grade','Age','Gender', 'FormalClothing','Pant','Trackpant','Halfshirt','Blazer','Jacket','Skirt',
'FullShirt','HalfSweater','Tshirt','Socks','Thermal','Vest','FullSweater','SwC','MC'],
info='Select the features to be freezed to generate CFs')
model_I = gr.Dropdown(models, label='Model', info='Select the model to generate CFs')
button_cf_I = gr.Button(label="Generate CFs")
button_vis.click(read_csv, df_vis, outputs=output_vis)
button_model.click(train_model, [input_df, target, test_size, model_name, features_to_drop], outputs=model_output)
button_cf_T.click(generate_cfs_total, [input_df_T, target_T, radio_T, predefined_T,
custom_T, dropped_features_T, freeze_features_T, model_T],
outputs=output_T)
demo.launch() |