test my dev

.streamlit/config.toml

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+[theme]
+base="light"

li_analysys.py

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+
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import zscore
+st.set_page_config(layout="wide")
+
+def categorize_marks(normalized_marks):
+    if normalized_marks >= 0.8:
+        return '80-100%'
+    elif normalized_marks >= 0.6:
+        return '60-80%'
+    else:
+        return '<60%'
+
+def analyze_student_performance_by_li(question_data, student_performance_data):
+    # Merge the dataframes on question number
+    merged_data = pd.merge(student_performance_data, question_data, on='question_number')
+    
+    merged_data = merged_data.groupby(["student_id","learning_indicator_id"])[["marks_obtained","maximum_marks"]].sum().reset_index()
+    merged_data['normalized_marks'] = merged_data['marks_obtained'] / merged_data['maximum_marks']
+    # Categorize the normalized marks
+    merged_data['category'] = merged_data['normalized_marks'].apply(categorize_marks)
+    
+    # Group by learning indicator ID and category, and count the number of students in each category
+    merged_data = merged_data.groupby(['learning_indicator_id', 'category']).size().unstack(fill_value=0)
+    
+    # Rename the columns for better readability
+    merged_data.columns = ['<60%', '60-80%', '80-100%']
+    merged_data = merged_data.sort_values(['<60%', '60-80%', '80-100%'],ascending=[False,False,False]).reset_index()
+    
+    # Display the results
+    return merged_data
+
+def prioritize_lis(category_counts):
+    # Add a rank column based on the order of rows
+    category_counts['Rank'] = category_counts.index + 1
+    
+    # Determine the number of LIs
+    total_lis = len(category_counts)
+    
+    # Determine the cutoff points for high, medium, and low priority
+    high_priority_cutoff = int(total_lis * 0.3)
+    medium_priority_cutoff = int(total_lis * 0.6)
+    
+    # Classify the LIs based on their rank
+    category_counts['Priority'] = 'Low'
+    category_counts.loc[category_counts['Rank'] <= high_priority_cutoff, 'Priority'] = 'High'
+    category_counts.loc[(category_counts['Rank'] > high_priority_cutoff) & (category_counts['Rank'] <= medium_priority_cutoff), 'Priority'] = 'Medium'
+    
+    return category_counts
+
+def mean_li_level_analysis(student_data, question_data):
+    merged_data = pd.merge(student_data, question_data, on='question_number',how="inner")
+    
+    # Normalize the marks obtained for each learning indicator by each student
+    merged_data = merged_data.groupby(['learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()
+    merged_data["normalised_score"]=merged_data["marks_obtained"]/merged_data["maximum_marks"]
+    merged_data = pd.merge(merged_data,question_data[["learning_indicator_id","learning_indicator_text"]].drop_duplicates(),on="learning_indicator_id")
+    return merged_data
+
+
+
+def student_level_analysis(student_data, question_data, prioritized_lis):
+    # Merge the student data with question data
+    merged_data = pd.merge(student_data, question_data, on='question_number',how="inner")
+    
+    # Normalize the marks obtained for each learning indicator by each student
+    merged_data = merged_data.groupby(['student_id', 'learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()
+    merged_data['normalized_marks'] = merged_data['marks_obtained'] / merged_data['maximum_marks']
+    merged_data = merged_data[merged_data["normalized_marks"]<0.80]
+    
+    # Merge with prioritized_lis to get the priority and rank
+    merged_data = pd.merge(merged_data, prioritized_lis[['learning_indicator_id', 'Rank']], on='learning_indicator_id', how='left')
+    
+    # Rank the LIs for each student based on normalized marks and class-level LI priority
+    merged_data['student_rank'] = merged_data.groupby('student_id')['normalized_marks'].rank(method='dense', ascending=False)
+    merged_data = merged_data.sort_values(by=['student_id', 'student_rank', 'Rank'])
+    
+    # Ensure unique ranks by adding a secondary sort by Rank
+    merged_data['unique_rank'] = merged_data.groupby('student_id').cumcount() + 1
+    
+    # Create the final dataframe
+    student_ranking = merged_data.pivot(index='student_id', columns='unique_rank', values='learning_indicator_id').reset_index()
+    student_ranking.columns = ['student_id'] + [f'P{I+1}_li' for I in range(student_ranking.shape[1] - 1)]
+    
+    li_text_mapping = question_data.drop_duplicates(subset='learning_indicator_id').set_index('learning_indicator_id')['learning_indicator_text']
+
+    for col in student_ranking.columns[1:]:
+        student_ranking[col] = student_ranking[col].map(li_text_mapping)
+    return student_ranking
+
+
+def prepare_data_for_ridge_plot(student_data, question_data):
+    # Merge the DataFrames
+    merged_data = pd.merge(student_data, question_data, on='question_number', how='inner')
+    
+    # Normalize the marks obtained for each learning indicator by each student
+    normalized_data = merged_data.groupby(['student_id', 'learning_indicator_id'])[['marks_obtained', 'maximum_marks']].sum().reset_index()
+    normalized_data['normalized_marks'] = normalized_data['marks_obtained'] / normalized_data['maximum_marks']
+    
+    # Add learning_indicator_text to normalized_data
+    plot_data = pd.merge(normalized_data, question_data[['learning_indicator_id', 'learning_indicator_text']].drop_duplicates(), on='learning_indicator_id')
+    
+    return plot_data
+
+def calculate_logical_quantiles(data, num_quantiles=5):
+    """
+    Calculate logical quantiles for a given data set to ensure they are informative.
+    
+    Parameters:
+    data (array-like): The input data for which to calculate quantiles.
+    num_quantiles (int): The number of quantiles to calculate. Default is 5.
+    
+    Returns:
+    list: A list of quantile values.
+    """
+    # Ensure there are enough unique values to calculate the quantiles
+    if len(np.unique(data)) < num_quantiles:
+        # If not enough unique values, use unique values as quantiles
+        quantiles = np.unique(data)
+    else:
+        # Calculate evenly spaced quantiles
+        quantiles = np.percentile(data, np.linspace(0, 100, num_quantiles))
+    
+    return quantiles.tolist()
+
+def create_ridge_plot(plot_data):
+    unique_learning_indicators = plot_data['learning_indicator_text'].unique()
+    n_indicators = len(unique_learning_indicators)
+    bandwidth = 0.5  # Adjust bandwidth for smoother graphs
+    darkgreen = '#9BC184'
+    midgreen = '#C2D6A4'
+    lightgreen = '#E7E5CB'
+    colors = [lightgreen, midgreen, darkgreen, midgreen, lightgreen]
+
+    fig, axs = plt.subplots(nrows=n_indicators, ncols=1, figsize=(10, n_indicators * 1.5), sharex=True)
+    axs = axs.flatten()  # Flatten in case of single plot
+
+    for i, indicator in enumerate(unique_learning_indicators):
+        # Subset the data for each learning indicator
+        subset = plot_data[plot_data['learning_indicator_text'] == indicator]
+
+        # Plot the distribution of normalized marks
+        sns.kdeplot(
+            subset['normalized_marks'],
+            shade=True,
+            bw_adjust=bandwidth,
+            ax=axs[i],
+            color=sns.color_palette('coolwarm', n_colors=n_indicators)[i]
+        )
+        quantiles = calculate_logical_quantiles(subset["normalized_marks"].tolist())
+
+        # fill space between each pair of quantiles
+        for j in range(len(quantiles) - 1):
+            axs[i].fill_between(
+                [quantiles[j], # lower bound
+                quantiles[j+1]], # upper bound
+                0.1, # max y=0
+                0.3, # max y=0.0002
+                color=colors[j]
+            )
+        mean = subset['marks_obtained'].sum()/subset['maximum_marks'].sum()
+        axs[i].scatter([mean], [0.3], color='black', s=15)
+
+        global_mean = plot_data['normalized_marks'].mean()
+        axs[i].axvline(global_mean, color='#525252', linestyle='--')
+
+
+        axs[i].set_xlim(0, 1)
+        axs[i].set_ylim(0,3)
+
+        # Add the learning indicator text as the title
+        axs[i].set_title(indicator, loc='left', fontsize=12, fontweight='bold')
+
+        # Remove y-axis label
+        axs[i].set_ylabel('')
+
+        # Add a horizontal line for the baseline
+        axs[i].axhline(0, color='black', linewidth=1.3, linestyle='-')
+
+    # Set common labels
+    plt.xlabel('Normalized Marks', fontsize=12, fontweight='bold')
+
+    
+    plt.tight_layout()
+    return fig
+
+def remediation_groups(student_df, question_df, z_threshold=-1.35):
+    # Merge student performance with question data to get full marks
+    student_df["student_id"]=student_df["student_id"].astype(str)
+    merged_df = pd.merge(student_df, question_df, on='question_number')
+    
+    # Apply minimum marks validation and fill NaN with 0
+    merged_df['marks_obtained'] = np.minimum(merged_df['marks_obtained'].fillna(0), merged_df['full_marks'])
+    
+    # Calculate normalized scores
+    merged_df['normalized_score'] = merged_df['marks_obtained'] / merged_df['full_marks']
+    
+    # Calculate z-scores for each learning indicator
+    z_scores = merged_df.groupby('learning_indicator_id')['normalized_score'].transform(zscore)
+    merged_df['z_score'] = z_scores
+    
+    # Identify students needing remediation
+    remediation_df = merged_df[merged_df['z_score'] < z_threshold]
+    
+    # Group by learning indicator to find students needing remediation
+    li_remediation_groups = remediation_df.groupby(['learning_indicator_id', 'learning_indicator_text'])['student_id'].apply(lambda x: ', '.join(x.unique())).reset_index()
+    
+    # Identify students who don't need remediation
+    students_needing_remediation = remediation_df['student_id'].unique()
+    students_no_remediation = merged_df[~merged_df['student_id'].isin(students_needing_remediation)]['student_id'].unique()
+    no_remediation_df = pd.DataFrame(students_no_remediation, columns=['student_id'])
+    
+    return li_remediation_groups, no_remediation_df
+
+def main():
+    col_logo , col_name = st.columns([1,3])
+    with col_logo:
+        st.image("Screenshot 2024-08-07 at 1.05.24 PM.png")
+    with col_name:
+        st.title("Learning Indicator Analysis")
+        st.subheader("Student and Class Remediation based on Question Data and Student Data. Upload Files")
+
+    # Upload the dataframes
+    col_A,colB = st.columns(2)
+    with col_A:
+        question_data_file = st.file_uploader("Upload Question Data CSV", type="csv")
+    with colB:
+        student_performance_data_file = st.file_uploader("Upload Student Performance Data CSV", type="csv")
+    st.write("----------------------")
+    
+    if question_data_file and student_performance_data_file:
+        question_data = pd.read_csv(question_data_file)
+        student_performance_data = pd.read_csv(student_performance_data_file)
+        
+        # Analyze performance and prioritize LIs
+        category_counts = analyze_student_performance_by_li(question_data, student_performance_data)
+        prioritized_lis = prioritize_lis(category_counts)
+        
+        # Merge with original question data to get the learning indicator text
+        prioritized_lis = pd.merge(prioritized_lis, question_data[['learning_indicator_id', 'learning_indicator_text']].drop_duplicates(), on='learning_indicator_id', how='left')
+        
+        # Display the results with filters
+        st.write("Learning Indicator Analysis with Priority")
+        
+        def highlight_priority(row):
+            if row.Priority == 'High':
+                return ['background-color: red']*len(row)
+            elif row.Priority == 'Medium':
+                return ['background-color: yellow']*len(row)
+            elif row.Priority == 'Low':
+                return ['background-color: green']*len(row)
+            else:
+                return ['']*len(row)
+            
+        col1,col2 = st.columns(2)
+        with col1:
+            st.dataframe(prioritized_lis.style.apply(highlight_priority, axis=1))
+            overall_li_level = mean_li_level_analysis(student_performance_data, question_data)
+            overall_li_level = overall_li_level.sort_values("normalised_score")
+            st.dataframe(overall_li_level)
+        with col2:
+            plt_data=prepare_data_for_ridge_plot(student_performance_data, question_data)
+            plt_fig = create_ridge_plot(plt_data)
+            st.pyplot(plt_fig)
+        st.write("---------------------------")
+        col3,col4 = st.columns(2)
+        li_remediation_groups, no_remediation_df = remediation_groups(student_performance_data,question_data)
+        with col3:
+            st.write("Student Group Remediation based on LI")
+            st.dataframe(li_remediation_groups)
+        with col4:
+            st.write("Students That are not part of group remediation")
+            st.dataframe(no_remediation_df)
+
+        
+        # Filters for LI ID and Priority
+        li_id_filter = st.multiselect("Exclude Li_ids :", prioritized_lis['learning_indicator_id'].unique())
+        priority_filter = st.multiselect("Exclude  Priority:",prioritized_lis["Priority"].unique())
+        if li_id_filter:
+            prioritized_lis = prioritized_lis[~prioritized_lis["learning_indicator_id"].isin(li_id_filter)]
+            question_data = question_data[~question_data["learning_indicator_id"].isin(li_id_filter)]
+        if priority_filter:
+            li_ids_out = prioritized_lis[prioritized_lis["Priority"].isin(priority_filter)]["learning_indicator_id"].unique().tolist()
+            question_data = question_data[~question_data["learning_indicator_id"].isin(li_ids_out)]
+
+
+        
+        # Button to generate student-level ranking
+        if st.button("Generate Student Level Ranking"):
+            print(len(question_data),"==question")
+            print(len(prioritized_lis),"===priotisex")
+            student_ranking = student_level_analysis(student_performance_data, question_data, prioritized_lis)
+            st.write("Student Level Learning Indicator Ranking")
+            st.dataframe(student_ranking)
+    
+if __name__ == "__main__":
+    main()

question_paper.csv

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+question_number,full_marks,learning_indicator_id,learning_indicator_text
+1,5,LI001,Understand basic algebraic concepts
+2,4,LI002,Apply geometric principles
+3,3,LI003,Solve linear equations
+4,6,LI004,Interpret statistical data
+5,5,LI005,Analyze historical events
+6,4,LI006,Understand economic theories
+7,3,LI007,Apply scientific method
+8,6,LI008,Comprehend literary texts
+9,5,LI001,Understand basic algebraic concepts
+10,4,LI002,Apply geometric principles
+11,3,LI003,Solve linear equations
+12,6,LI004,Interpret statistical data
+13,5,LI005,Analyze historical events
+14,4,LI006,Understand economic theories
+15,3,LI007,Apply scientific method

requirements.txt

@@ -0,0 +1,73 @@
+altair==5.3.0
+appnope==0.1.4
+asttokens==2.4.1
+attrs==24.1.0
+blinker==1.8.2
+cachetools==5.4.0
+certifi==2024.7.4
+charset-normalizer==3.3.2
+click==8.1.7
+comm==0.2.2
+contourpy==1.2.1
+cycler==0.12.1
+debugpy==1.8.3
+decorator==5.1.1
+exceptiongroup==1.2.2
+executing==2.0.1
+fonttools==4.53.1
+gitdb==4.0.11
+GitPython==3.1.43
+idna==3.7
+ipykernel==6.29.5
+ipython==8.26.0
+jedi==0.19.1
+Jinja2==3.1.4
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+jupyter_client==8.6.2
+jupyter_core==5.7.2
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.1
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+nest-asyncio==1.6.0
+numpy==2.0.1
+packaging==24.1
+pandas==2.2.2
+parso==0.8.4
+pexpect==4.9.0
+pillow==10.4.0
+platformdirs==4.2.2
+prompt_toolkit==3.0.47
+protobuf==5.27.3
+psutil==6.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==17.0.0
+pydeck==0.9.1
+Pygments==2.18.0
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+pytz==2024.1
+pyzmq==26.1.0
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.19.1
+scipy==1.14.0
+seaborn==0.13.2
+six==1.16.0
+smmap==5.0.1
+stack-data==0.6.3
+streamlit==1.37.0
+tenacity==8.5.0
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4.1
+traitlets==5.14.3
+typing_extensions==4.12.2
+tzdata==2024.1
+urllib3==2.2.2
+wcwidth==0.2.13

students_df.csv

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+question_number,student_id,marks_obtained,maximum_marks
+1,1,3,5
+2,1,1,4
+3,1,0,3
+4,1,6,6
+5,1,1,5
+6,1,1,4
+7,1,3,3
+8,1,6,6
+9,1,4,5
+10,1,4,4
+11,1,3,3
+12,1,2,6
+13,1,5,5
+14,1,0,4
+15,1,0,3
+1,2,2,5
+2,2,4,4
+3,2,0,3
+4,2,4,6
+5,2,2,5
+6,2,1,4
+7,2,3,3
+8,2,1,6
+9,2,4,5
+10,2,1,4
+11,2,3,3
+12,2,3,6
+13,2,5,5
+14,2,0,4
+15,2,2,3
+1,3,4,5
+2,3,1,4
+3,3,1,3
+4,3,4,6
+5,3,0,5
+6,3,1,4
+7,3,3,3
+8,3,6,6
+9,3,3,5
+10,3,0,4
+11,3,1,3
+12,3,2,6
+13,3,2,5
+14,3,3,4
+15,3,3,3
+1,4,3,5
+2,4,4,4
+3,4,0,3
+4,4,6,6
+5,4,2,5
+6,4,2,4
+7,4,2,3
+8,4,1,6
+9,4,3,5
+10,4,3,4
+11,4,0,3
+12,4,4,6
+13,4,1,5
+14,4,4,4
+15,4,3,3
+1,5,4,5
+2,5,1,4
+3,5,1,3
+4,5,6,6
+5,5,1,5
+6,5,3,4
+7,5,0,3
+8,5,0,6
+9,5,3,5
+10,5,0,4
+11,5,2,3
+12,5,3,6
+13,5,0,5
+14,5,3,4
+15,5,2,3
+1,6,3,5
+2,6,2,4
+3,6,3,3
+4,6,4,6
+5,6,4,5
+6,6,2,4
+7,6,2,3
+8,6,4,6
+9,6,1,5
+10,6,3,4
+11,6,0,3
+12,6,3,6
+13,6,5,5
+14,6,1,4
+15,6,3,3
+1,7,4,5
+2,7,0,4
+3,7,3,3
+4,7,2,6
+5,7,4,5
+6,7,2,4
+7,7,3,3
+8,7,1,6
+9,7,2,5
+10,7,2,4
+11,7,1,3
+12,7,4,6
+13,7,3,5
+14,7,1,4
+15,7,1,3
+1,8,0,5
+2,8,2,4
+3,8,0,3
+4,8,2,6
+5,8,3,5
+6,8,4,4
+7,8,1,3
+8,8,3,6
+9,8,3,5
+10,8,1,4
+11,8,3,3
+12,8,3,6
+13,8,4,5
+14,8,3,4
+15,8,0,3
+1,9,3,5
+2,9,2,4
+3,9,2,3
+4,9,4,6
+5,9,1,5
+6,9,0,4
+7,9,0,3
+8,9,5,6
+9,9,2,5
+10,9,2,4
+11,9,0,3
+12,9,2,6
+13,9,3,5
+14,9,1,4
+15,9,2,3
+1,10,4,5
+2,10,0,4
+3,10,3,3
+4,10,2,6
+5,10,1,5
+6,10,1,4
+7,10,3,3
+8,10,4,6
+9,10,0,5
+10,10,2,4
+11,10,2,3
+12,10,1,6
+13,10,0,5
+14,10,2,4
+15,10,2,3
+1,11,4,5
+2,11,3,4
+3,11,1,3
+4,11,4,6
+5,11,2,5
+6,11,0,4
+7,11,2,3
+8,11,2,6
+9,11,3,5
+10,11,4,4
+11,11,0,3
+12,11,3,6
+13,11,2,5
+14,11,3,4
+15,11,3,3
+1,12,3,5
+2,12,1,4
+3,12,3,3
+4,12,5,6
+5,12,2,5
+6,12,2,4
+7,12,1,3
+8,12,2,6
+9,12,2,5
+10,12,1,4
+11,12,3,3
+12,12,6,6
+13,12,0,5
+14,12,0,4
+15,12,2,3
+1,13,3,5
+2,13,1,4
+3,13,3,3
+4,13,2,6
+5,13,5,5
+6,13,4,4
+7,13,3,3
+8,13,6,6
+9,13,1,5
+10,13,2,4
+11,13,0,3
+12,13,6,6
+13,13,0,5
+14,13,2,4
+15,13,3,3
+1,14,3,5
+2,14,2,4
+3,14,1,3
+4,14,1,6
+5,14,0,5
+6,14,4,4
+7,14,0,3
+8,14,0,6
+9,14,5,5
+10,14,1,4
+11,14,0,3
+12,14,0,6
+13,14,0,5
+14,14,0,4
+15,14,1,3
+1,15,0,5
+2,15,2,4
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