Upload 4_Compare.py

pages/4_Compare.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+st.title("Compare Your Algorithm")
+
+default_dataOne = {
+    "X": [1, 2, 3, 4, 5],
+    "Y": [2.2, 4.4, 6.5, 8.0, 10.1],
+    "Select": [True, True, True, True, True] 
+}
+default_dataTwo = {
+    "X": [1, 10, 100, 1000, 10000],
+    "Y": [3.3, 6.6, 12.21, 24.84, 48.25],
+    "Select": [True, True, True, True, True]
+}
+dataOne = pd.DataFrame(default_dataOne)
+dataTwo = pd.DataFrame(default_dataTwo)
+
+xlabel = st.text_input("X-axis", "X")
+ylabel = st.text_input("Y-axis", "Y")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Enter Your Data One")
+    cola, colb = st.columns(2)
+    with cola:
+        user_dataOne = st.data_editor(dataOne, num_rows="dynamic", key="data_editor_one")
+    with colb:
+        fit_typeOne = st.radio(
+            "Choose the Type of Fit",
+            options=["Logarithmic", "Linear", "Linearithmic", "Quadratic", "Cubic", "Exponential"],
+            index=1,
+            key="one"
+        )
+
+with col2:
+    st.subheader("Enter Your Data Two")
+    colc, cold = st.columns(2)
+    with colc:
+        user_dataTwo = st.data_editor(dataTwo, num_rows="dynamic", key="data_editor_two")
+    with cold:
+        fit_typeTwo = st.radio(
+            "Choose the Type of Fit",
+            options=["Logarithmic", "Linear", "Linearithmic", "Quadratic", "Cubic", "Exponential"],
+            index=0,
+            key="two"
+        )
+
+try:
+    selected_dataOne = user_dataOne[user_dataOne["Select"]]
+    x = np.array(selected_dataOne["X"], dtype=float)
+    y = np.array(selected_dataOne["Y"], dtype=float)
+
+    if len(x) < 2 and len(y) < 2:
+        st.warning("Please enter at least 2 data points.")
+        st.stop()
+except ValueError:
+    st.error("Invalid data entered. Please ensure all values are numeric.")
+    st.stop()
+
+try:
+    selected_dataTwo = user_dataTwo[user_dataTwo["Select"]]
+    u = np.array(selected_dataTwo["X"], dtype=float)
+    v = np.array(selected_dataTwo["Y"], dtype=float)
+
+    if len(u) < 2 and len(v) < 2:
+        st.warning("Please enter at least 2 data points.")
+        st.stop()
+except ValueError:
+    st.error("Invalid data entered. Please ensure all values are numeric.")
+    st.stop()
+
+if fit_typeOne == "Logarithmic":
+    try:
+        log_x = np.log(x)
+        coefficients = np.polyfit(log_x, y , 1)
+        y_fit = coefficients[0] * log_x + coefficients[1]
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {coefficients[0]:.4f}*log(x) + {coefficients[1]:.4f}"
+    except ValueError:
+        st.error("Logarithmic fit failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_typeOne == "Linear":
+    degree = 1
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.4f}*x + {coefficients[1]:.4f}"
+
+elif fit_typeOne == "Linearithmic":
+    try:
+        x_log_x = x * np.log(x)
+        A = np.column_stack((x_log_x, x, np.ones_like(x)))
+        coefficients, _, _, _ = np.linalg.lstsq(A, y, rcond=None)
+        a, b, c = coefficients
+        y_fit = a * x_log_x + b * x + c
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {a:.4f}*x*log(x) + {b:.4f}*x + {c:.4f}"
+    except ValueError:
+        st.error("Linearithmic fir failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_typeOne == "Quadratic":
+    degree = 2
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.4f}*x² + {coefficients[1]:.4f}*x + {coefficients[2]:.4f}"
+
+elif fit_typeOne == "Cubic":
+    degree = 3
+    coefficients = np.polyfit(x, y, degree)
+    y_fit = np.polyval(coefficients, x)
+    r2 = r2_score(y, y_fit)
+    equation = f"y = {coefficients[0]:.4f}*x³ + {coefficients[1]:.4f}*x² + {coefficients[2]:.4f}*x + {coefficients[3]:.4f}"
+
+elif fit_typeOne == "Exponential":
+    try:
+        log_y = np.log(y)
+        coefficients = np.polyfit(x, log_y, 1)
+        a = np.exp(coefficients[1])
+        b = coefficients[0]
+        y_fit = a * np.exp(b * x)
+        r2 = r2_score(y, y_fit)
+        equation = f"y = {a:.4f}*exp({b:.4f}*x)"
+    except ValueError:
+        st.error("Exponential fit failed. Ensure all Y values are positive.")
+        st.stop()
+
+if fit_typeTwo == "Logarithmic":
+    try:
+        log_u = np.log(u)
+        coefficients_Two = np.polyfit(log_u, v , 1)
+        v_fit = coefficients_Two[0] * log_u + coefficients_Two[1]
+        r2_Two = r2_score(v, v_fit)
+        equation_Two = f"y = {coefficients_Two[0]:.4f}*log(x) + {coefficients_Two[1]:.4f}"
+    except ValueError:
+        st.error("Logarithmic fit failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_typeTwo == "Linear":
+    degree_Two = 1
+    coefficients_Two = np.polyfit(u, v, degree_Two)
+    v_fit = np.polyval(coefficients_Two, u)
+    r2_Two = r2_score(v, v_fit)
+    equation_Two = f"y = {coefficients_Two[0]:.4f}*x + {coefficients_Two[1]:.4f}"
+
+elif fit_typeTwo == "Linearithmic":
+    try:
+        u_log_u = u * np.log(u)
+        B = np.column_stack((u_log_u, u, np.ones_like(u)))
+        coefficients_Two, _, _, _ = np.linalg.lstsq(B, v, rcond=None)
+        d, e, f = coefficients_Two
+        v_fit = d * u_log_u + e * u + f
+        r2_Two = r2_score(v, v_fit)
+        equation_Two = f"y = {d:.4f}*x*log(x) + {e:.4f}*x + {f:.4f}"
+    except ValueError:
+        st.error("Linearithmic fir failed. Ensure all X values are positive.")
+        st.stop()
+
+elif fit_typeTwo == "Quadratic":
+    degree_Two = 2
+    coefficients_Two = np.polyfit(u, v, degree_Two)
+    v_fit = np.polyval(coefficients_Two, u)
+    r2_Two = r2_score(v, v_fit)
+    equation_Two = f"y = {coefficients_Two[0]:.4f}*x² + {coefficients_Two[1]:.4f}*x + {coefficients_Two[2]:.4f}"
+
+elif fit_typeTwo == "Cubic":
+    degree_Two = 3
+    coefficients_Two = np.polyfit(u, v, degree_Two)
+    v_fit = np.polyval(coefficients_Two, u)
+    r2_Two = r2_score(v, v_fit)
+    equation_Two = f"y = {coefficients_Two[0]:.4f}*x³ + {coefficients_Two[1]:.4f}*x² + {coefficients_Two[2]:.4f}*x + {coefficients_Two[3]:.4f}"
+
+elif fit_typeTwo == "Exponential":
+    try:
+        log_v = np.log(v)
+        coefficients_Two = np.polyfit(u, log_v, 1)
+        d = np.exp(coefficients_Two[1])
+        e = coefficients_Two[0]
+        v_fit = d * np.exp(e * u)
+        r2_Two = r2_score(v, v_fit)
+        equation_Two = f"y = {d:.4f}*exp({e:.4f}*x)"
+    except ValueError:
+        st.error("Exponential fit failed. Ensure all Y values are positive.")
+        st.stop()
+
+minimum = min(min(x), min(u))
+maximum = max(max(x), max(u))
+x_smooth = np.linspace(minimum, maximum, 500)
+if fit_typeOne == "Logarithmic":
+    y_smooth = coefficients[0] * np.log(x_smooth) + coefficients[1]
+elif fit_typeOne == "Linearithmic":
+    y_smooth = a * x_smooth * np.log(x_smooth) + b * x_smooth + c
+elif fit_typeOne == "Exponential":
+    y_smooth = a * np.exp(b * x_smooth)
+else:
+    y_smooth = np.polyval(coefficients, x_smooth)
+
+u_smooth = np.linspace(minimum, maximum, 500)
+if fit_typeTwo == "Logarithmic":
+    v_smooth = coefficients_Two[0] * np.log(u_smooth) + coefficients_Two[1]
+elif fit_typeTwo == "Linearithmic":
+    v_smooth = d * u_smooth * np.log(u_smooth) + e * u_smooth + f
+elif fit_typeTwo == "Exponential":
+    v_smooth = d * np.exp(e * u_smooth)
+else:
+    v_smooth = np.polyval(coefficients_Two, u_smooth)
+
+fig, ax = plt.subplots()
+ax.scatter(x, y, color="red", label="Original Data One")
+ax.scatter(u, v, color="blue", label="Original Data Two")
+ax.plot(x_smooth, y_smooth, color="pink", label=f"{fit_typeOne} Fit (R²={r2:.4f})")
+ax.plot(u_smooth, v_smooth, color="purple", label=f"{fit_typeTwo} Fit (R²={r2_Two:.4f})")
+ax.set_xlabel(xlabel)
+ax.set_ylabel(ylabel)
+ax.legend()
+ax.set_title("fit")
+
+st.pyplot(fig)
+
+st.write(f"**Fitted Equation One**: {equation}")
+st.write(f"**R² Value One**: {r2:.6f}")
+
+st.write(f"**Fitted Equation Two**: {equation_Two}")
+st.write(f"**R² Value Two**: {r2_Two:.6f}")