Delete pages/Dashboard.py

pages/Dashboard.py

@@ -1,177 +0,0 @@
-import streamlit as st
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-from Utility.data_loader  import load_train_series,load_train_events,load_sample_submission,load_test_series
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, StandardScaler
-from xgboost import XGBClassifier  # or XGBRegressor depending on your task
-import xgboost as xgb
-import numpy as np
-
-@st.cache_data
-def load_sampled_data():
-    df3 = pd.read_parquet("train_series.parquet", columns=['series_id', 'step', 'anglez', 'enmo'])
-    df4 = pd.read_parquet("test_series.parquet", columns=['series_id', 'step', 'anglez', 'enmo'])
-    df2 = pd.read_csv("train_events.csv")
-
-    # Sample safely based on available data
-    df3_sample = df3.sample(n=min(5_000_000, len(df3)), random_state=42)
-    df4_sample = df4.sample(n=min(1_000_000, len(df4)), random_state=42)
-
-    return df3_sample, df4_sample, df2
-
-# Load
-df3, df4, df2 = load_sampled_data()
-df = pd.concat([df3, df4], axis=0, ignore_index=True)
-merged_df = pd.merge(df, df2, on=['series_id', 'step'], how='inner')
-
-# Rename timestamp columns if they exist
-if 'timestamp_x' in merged_df.columns:
-    merged_df.rename(columns={'timestamp_x': 'sensor_timestamp'}, inplace=True)
-if 'timestamp_y' in merged_df.columns:
-    merged_df.rename(columns={'timestamp_y': 'event_timestamp'}, inplace=True)
-
-# Box plots for each numerical feature
-fig, ax = plt.subplots(figsize=(2, 1))
-sns.boxplot(x=df2['step'], ax=ax)
-ax.set_title('Boxplot of Step')
-
-# Show the plot in Streamlit
-st.pyplot(fig)
-
-st.write("1. Data Visualization - Scatter Plot (feature vs feature or vs target)")
-# Assume merged_df is already defined or loaded
-df_sample = merged_df  # or use df_sample = merged_df.sample(n=50000) to downsample
-
-st.subheader("Scatter Plot: anglez vs enmo")
-
-# Create the plot
-fig, ax = plt.subplots(figsize=(10, 6))
-sns.scatterplot(x='anglez', y='enmo', data=df_sample, ax=ax)
-ax.set_title("Scatter Plot: anglez vs enmo")
-
-# Display in Streamlit
-st.pyplot(fig)
-
-# df_sample = merged_df.sample(n=10000)  # adjust sample size for performance
-
-# # Subheader
-# st.subheader("Pair Plot of Features")
-
-# # Create pairplot
-# fig = sns.pairplot(df_sample[['anglez', 'enmo', 'step']])
-# fig.fig.suptitle("Pair Plot of Features", y=1.02)
-
-# # Display in Streamlit
-# st.pyplot(fig)
-# Define columns to plot
-plot_columns = ['anglez', 'enmo', 'step']
-
-# Safety check: make sure required columns exist
-if all(col in merged_df.columns for col in plot_columns):
-    
-    # Check data size and sample accordingly
-    max_rows = len(merged_df)
-    sample_size = min(10000, max_rows)  # Don't exceed available rows
-
-    df_sample = merged_df.sample(n=sample_size)
-
-    # Subheader
-    st.subheader("Pair Plot of Features")
-
-    # Create pairplot
-    fig = sns.pairplot(df_sample[plot_columns])
-    fig.fig.suptitle("Pair Plot of Features", y=1.02)
-
-    # Display in Streamlit
-    st.pyplot(fig)
-
-else:
-    st.error("One or more required columns ('anglez', 'enmo', 'step') are missing in the dataset.")
-
-
-  
-
-# Define features to plot
-plot_features = ['anglez', 'enmo']
-
-# Check if the required columns exist in the DataFrame
-if all(col in merged_df.columns for col in plot_features):
-    total_rows = len(merged_df)
-    sample_size = 10000
-
-    # Handle small datasets
-    if total_rows < sample_size:
-        st.info(f"Only {total_rows} rows available — using full dataset.")
-        df_sample = merged_df.copy()
-    else:
-        df_sample = merged_df.sample(n=sample_size)
-
-    # Plot
-    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
-
-    sns.histplot(df_sample['anglez'], kde=True, bins=50, ax=axes[0])
-    axes[0].set_title("Distribution of anglez")
-
-    sns.histplot(df_sample['enmo'], kde=True, bins=50, ax=axes[1])
-    axes[1].set_title("Distribution of enmo")
-
-    plt.tight_layout()
-    st.pyplot(fig)
-
-else:
-    st.error("Required columns not found in the dataset.")
-
-
-    
-
-st.write("Multicollinearity Check - Correlation Matrix")
-features = ['anglez', 'enmo', 'step', 'night']
-df_subset = merged_df[features]
-
-# Streamlit title
-st.subheader("Multicollinearity Check - Correlation Matrix")
-
-# Calculate correlation matrix
-corr_matrix = df_subset.corr()
-
-# Plot heatmap
-fig, ax = plt.subplots(figsize=(6, 4))
-sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', ax=ax)
-ax.set_title("Correlation Matrix")
-
-# Display in Streamlit
-st.pyplot(fig)
-
-# Encode
-le = LabelEncoder()
-merged_df['series_id'] = le.fit_transform(merged_df['series_id'])
-merged_df['event'] = le.fit_transform(merged_df['event'])
-
-# Drop columns with string or datetime values
-drop_cols = ['sensor_timestamp', 'event_timestamp', 'night', 'step', 'sleep_duration_hrs', 'series_id']
-df_cleaned = merged_df.drop(columns=[col for col in drop_cols if col in merged_df.columns])
-
-# Ensure only numeric features in X
-X = df_cleaned.drop('event', axis=1).select_dtypes(include=[np.number])
-y = merged_df['event']
-
-# Split and scale
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=27)
-
-st.write("Feature Importance")
-# Create model instance
-xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='logloss')  # example for classification
-
-# Fit the model
-xgb_model.fit(X_train, y_train)
-
-# Plot feature importance
-fig, ax = plt.subplots()
-xgb.plot_importance(xgb_model, ax=ax)
-ax.set_title("XGBoost Feature Importance")
-
-# Show in Streamlit
-st.subheader("XGBoost Feature Importance")
-st.pyplot(fig)