init

.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,13 @@
+---
+title: Thermal Comfort
+emoji: 📚
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.36.1
+app_file: app.py
+pinned: false
+license: ms-pl
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,80 @@
+import gradio as gr
+import pandas as pd
+
+
+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):
+    import pickle
+
+    from sklearn.compose import ColumnTransformer
+    from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+    from sklearn.model_selection import train_test_split
+    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.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('./models/' + model_name + '.pkl', 'wb'))
+    return 'Model Saved'
+
+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")
+
+    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)
+
+
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+pandas
+scikit-learn