Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import plotly.express as px | |
| import pandas as pd | |
| from module.load_data import data | |
| from module.dataset import create_data | |
| import plotly.express as px | |
| from sklearn.tree import DecisionTreeClassifier, export_graphviz | |
| import matplotlib.pyplot as plt | |
| #plt.style.use('seaborn-dark-palette') | |
| def grades(mark): | |
| if mark >= 0 and mark < 5: | |
| return 'U' | |
| elif mark >= 5 and mark < 9: | |
| return 'E' | |
| elif mark >= 9 and mark < 12: | |
| return 'D' | |
| elif mark >= 12 and mark < 15: | |
| return 'C' | |
| elif mark >= 15 and mark < 18: | |
| return 'B' | |
| else: | |
| return 'A' | |
| def segmentation(data:pd.DataFrame): | |
| df = data.copy() | |
| factor = pd.factorize(df.form) | |
| df['form'] = factor[0]; definition=factor[1] | |
| cols = [f'seq {i+1}' for i in range(6)] + ['age','form'] | |
| tree = DecisionTreeClassifier(random_state=31012020) | |
| tree.fit(df[cols], df['gender']) | |
| export_graphviz(tree, out_file="images/tree.dot", class_names=["Female", "Male"], | |
| feature_names=cols, impurity=False, filled=True,rounded=True) | |
| result = pd.DataFrame({"Feature":cols, "Gain":tree.feature_importances_}) | |
| return result | |
| def scatterplot(female=None, male=None, line_separator=None, x_axis=None, y_axis=None): | |
| fig = plt.figure(figsize=(5,5)) | |
| plt.scatter(female[x_axis], female[y_axis], marker='v', color='red', label="Girl", lw=1.2) | |
| plt.scatter(male[x_axis], male[y_axis], marker='o', color='blue', label="Boy",lw=1.2) | |
| plt.xlabel(x_axis); plt.ylabel(y_axis) | |
| plt.title("Decision line", fontweight="bold") | |
| for u in line_separator[0]: | |
| plt.vlines(line_separator[0], 0, female[y_axis].max(), color="black", | |
| linestyles="-.", lw=1.75) | |
| for v in line_separator[1] : | |
| plt.hlines(line_separator[1], -1, female[y_axis].max(), color="black", | |
| linestyles="dashed", lw=1.75) | |
| plt.legend(loc="best", frameon=True, fancybox=True, shadow=True, title="Gender") | |
| return fig | |
| df_trim = create_data(data) | |
| total_students = len(data) | |
| average_age = data.age.mean() | |
| gca1 = df_trim["trimester 1"].mean() | |
| gca2 = df_trim["trimester 2"].mean() | |
| gca3 = df_trim["trimester 3"].mean() | |
| annual_gca = (gca1+gca2+gca3)/3 | |
| evaluations = ['seq 1', 'seq 2','seq 3','seq 4','seq 5','seq 6'] | |
| sgca = pd.DataFrame(columns=['Male','Female'], index=evaluations) | |
| sgca['Male'] = data[data.gender == 'M'][evaluations].mean() | |
| sgca['Female'] = data[data.gender == 'F'][evaluations].mean() | |
| form_counts = data.form.value_counts() | |
| gender_counts = data.gender.value_counts() | |
| progression = sgca.diff() | |
| pass_or_fail =pd.DataFrame({seq:data[seq].apply(lambda x: 'Passed' if x >= 10.0 else 'Failed').value_counts().to_dict() for seq in evaluations}) | |
| student_grade = pd.DataFrame({seq:data[seq].apply(grades).value_counts().to_dict() for seq in evaluations}) | |
| ##print(student_grade) | |
| bar_polar = px.bar_polar(data_frame=sgca, r='Male', | |
| theta=sgca.index, color='Female', | |
| title='General Class Average (GCA) for each evaluation', | |
| barmode="overlay",width=1200, height=500) | |
| pie_form = px.pie(data_frame=form_counts, names=form_counts.index, values=form_counts, | |
| title="Form", hole=0.25, width=500, height=500) | |
| pie_gender = px.pie(data_frame=gender_counts, names=gender_counts.index, values=gender_counts, | |
| title="Gender", hole=0.25, width=500, height=500) | |
| feature_importances = segmentation(data) | |
| bar_feature_importance = px.bar(data_frame=feature_importances, x="Feature", y="Gain", | |
| title="Feature importances bar", width=500) | |
| with open("images/tree.dot") as f: | |
| dot_graph = f.read() | |
| female = data[data.gender == "F"] | |
| male = data[data.gender == "M"] | |
| def visualization(): | |
| placeholder1 = st.empty() | |
| with placeholder1.container(): | |
| kp1, kp2, kp3, kp4, kp5, kp6 = st.columns(6) | |
| kp1.metric( | |
| label= "**:red[👬 Total students]**", | |
| value = total_students, | |
| delta = "" | |
| ) | |
| kp2.metric( | |
| label= ":red[**⏳ Average age**]", | |
| value = f"{average_age:.2f}" , | |
| delta = "" | |
| ) | |
| kp3.metric( | |
| label=":red[**✍ GCA-trimester 1**]", | |
| value=f"{gca1:.2f}/20", | |
| delta = 0.0 | |
| ) | |
| kp4.metric( | |
| label=":red[**✍ GCA-trimester 2**]", | |
| value = f"{gca2:.2f}/20", | |
| delta = round(gca2-gca1, 3) | |
| ) | |
| kp5.metric( | |
| label =":red[**✍ GCA-trimester 3**]", | |
| value = f"{gca3:.2f}/20", | |
| delta = round(gca3-gca2, 3) | |
| ) | |
| kp6.metric( | |
| label =":red[**🎓 Annual-GCA**]", | |
| value = f"{annual_gca:.2f}/20", | |
| delta = "" | |
| ) | |
| placeholder2 = st.empty() | |
| with placeholder2.container(): | |
| col1, col2 = st.columns(2) | |
| col1.plotly_chart(pie_gender) | |
| col1.caption('**We represent the gender of students in form 6e-5e (francophone section one of the cameroonian education system).**') | |
| col2.plotly_chart(pie_form) | |
| col2.caption("""**We represent the number of students in each form 6e and 5e**. | |
| **NB**: **Cameroon have two sub-education systems one is a francophone and a second is an anglosaxone.** | |
| """) | |
| tab1, tab2, tab3 = st.tabs([':orange[**Performance**]', ':orange[**Distribution**]', ':orange[**Miscelaneous**]']) | |
| with tab1: | |
| tab1.plotly_chart(bar_polar) | |
| tab1.caption("""We represent the general class average of students in each sequence. Colors bar shows the marks | |
| for female gender and bar polar also shows the marks for male gender.""") | |
| tab1.subheader('Progression') | |
| tab1.line_chart(progression) | |
| tab1.caption("""This chart shows a progression of students during the six evaluations. | |
| We just make a difference between the previous sequence and the next sequence. | |
| The x-axis represent a sequence and the y-axis represent the growth of students.""") | |
| tab1.subheader('Passed or Failed') | |
| col1, col2 = tab1.columns(2) | |
| col1.caption(':red[💃 passed or failed table 👉]') | |
| col1.dataframe(pass_or_fail) | |
| col1.caption(""" | |
| - In left: We got this table to compute the number of students that the mark is >10 (passed) or <10 (failed) for | |
| each sequence. | |
| - In right: We plot this table. The chart explains how a student make some effort to succeed a ICT course. | |
| - In general, we can appreciate the effort of the students in the form 6e and 5e for each evaluation. | |
| """) | |
| col2.bar_chart(pass_or_fail.T) | |
| tab1.subheader("Student grades") | |
| col3, col4 = tab1.columns(2) | |
| col3.caption(':red[💃 grades table 👉]') | |
| col3.dataframe(student_grade.fillna(0)) | |
| col3.caption(""" | |
| The student grade respect this decision: | |
| - U -> [0 - 5[; E -> [5 - 9[ | |
| - D -> [9 - 12[; C -> [12 - 15[ | |
| - B -> [15 - 18[; A -> [18 - 20[ | |
| """) | |
| col4.bar_chart(student_grade.T) | |
| col4.caption('This bar chart shows the number of student in each grade for each sequence.') | |
| with tab2: | |
| tab2.subheader('evaluation and age distribution') | |
| tab2.caption('Histogram') | |
| var1 = tab2.selectbox('Choose items', evaluations+['age'], key=6) | |
| fig1 = px.histogram(data_frame=data, x=var1, width=1200, height=500, opacity=0.75) | |
| tab2.plotly_chart(fig1) | |
| col1, col2 = tab2.columns(2) | |
| col1.caption('Boxplot'); col2.caption('Violin') | |
| var2 = col1.selectbox('Choose items', evaluations+['age'], key=7) | |
| var3 = col2.selectbox('Choose items', evaluations+['age'], key=8) | |
| col1.plotly_chart(px.box(data_frame=data, y=var2, width=500, height=500)) | |
| col2.plotly_chart(px.violin(data_frame=data, x=var3, width=500, height=500)) | |
| with tab3: | |
| st.subheader('Relation graph') | |
| container = tab3.empty() | |
| vcol1, vcol2, vcol3 = container.columns(3) | |
| trim1 = px.scatter(data_frame=df_trim, x="trimester 1", y="trimester 2", width=350, | |
| height=350,title="trimester 1 & 2") | |
| vcol1.plotly_chart(trim1) | |
| trim2 = px.scatter(data_frame=df_trim, x="trimester 2", y="trimester 3", width=350, height=350,title="trimester 3 & 2") | |
| vcol2.plotly_chart(trim2) | |
| trim3 = px.scatter(data_frame=df_trim, x="trimester 3", y="trimester 1", width=350, height=350,title="trimester 1 & 3") | |
| vcol3.plotly_chart(trim3) | |
| tab3.caption(""" | |
| The three charts shows the monotony of the relation function between the three trimesters. | |
| Each chart prove that the students for form 6e-5e francophone education systems at the Tebap college make considerably | |
| an effort to succeed an ICT's course. | |
| """) | |
| tab3.subheader('Supervised segmentation with tree') | |
| tab3.caption(""" | |
| In this section, we are making a supervised segmentation to segment the population of student into subgroups | |
| that have different values for the target gender. To find a subgroups, we are using a tree structured model. | |
| We cannot make a classification here because our dataset is just a size equal to 46. Let's go! 💂 | |
| """) | |
| block = tab3.empty() | |
| hcol1, hcol2 = block.columns(2) | |
| hcol1.caption('**Feature importance table**👉') | |
| hcol1.dataframe(feature_importances) | |
| hcol1.caption(""" | |
| The table shows the features that the tree structured model | |
| consider very importance for segmenting students | |
| population in to subgroups. | |
| """) | |
| hcol2.plotly_chart(bar_feature_importance) | |
| tab3.subheader('Dot graph') | |
| tab3.graphviz_chart(dot_graph) | |
| tab3.caption(""" | |
| Let's us interprete this graph. | |
| - node 0: the student that the mark in seq 6 <= 11.375 are female gender; the answer is False. In each node, we have | |
| a condition where the next node give an answers. | |
| Let's plot the line separating the region. | |
| """) | |
| bcol1, bcol2 = tab3.columns(2) | |
| var1 = bcol1.selectbox("Choose x-axis (line[i])", ['seq 1', "seq 2", "seq 4", "seq 6"]) | |
| var2 = bcol1.selectbox("Choose y-axis (line[i+1])", reversed(['seq 1', "seq 2", "seq 4", "seq 6"])) | |
| line1 = bcol2.multiselect("Line separator for x-axis", [1.25, 2.75, 3.125, 4.375,5.0, 5.625, 11.375,13.5]) | |
| line2 = bcol2.multiselect("Line separator for y-axis", [1.25, 2.75, 3.125, 4.375,5.0, 5.625, 11.375,13.5]) | |
| tab3.caption(""" | |
| We have eight lines line 1 (node 0) to line 8 (node 12). We start to line 1 and line 2. | |
| """) | |
| gplot = scatterplot(female, male, line_separator=[line1, line2], x_axis=var1, y_axis=var2) | |
| tab3.pyplot(gplot) | |